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Winter 2011THE SAUDI ARAMCO JOURNAL OF TECHNOLOGYA quarterly publication of the Saudi Arabian Oil CompanyInnovative CT Abrasive Hydrajetting Perforating Approach in a Complex SaudiArabian Gas Well Overcomes Initial Inability to Perform a Proppant FracturingTreatment and Achieves Better Than Expected Results: A Case Historysee page 2A Coiled Tubing Perforating Solution Incorporating a Gun Deployment Systemand Dynamic Underbalance Technique Improves Well Production in High AngleDeep Gas Wells in Saudi Arabiasee page 24Saudi AramcoJournal of Technology

Winter 2011THE SAUDI ARAMCO JOURNAL OF TECHNOLOGYA quarterly publication of the Saudi Arabian Oil CompanyContentsInnovative CT Abrasive Hydrajetting PerforatingApproach in a Complex Saudi Arabian Gas WellOvercomes Initial Inability to Perform a ProppantFracturing Treatment and Achieves Better Than ExpectedResults: A Case History 2J. Ricardo Solares, Walter Nuñez Garcia, Ataur R. Malik,Jose R. Vielma-Guillen, Alejandro Chacon and Craig WolfeA Novel Enzyme Breaker for Mud Cake Removal in HighTemperature Horizontal and Multilateral Wells 10Dr. Mohammed H. Al-Khadi, Dr. Biswesar Ghosh and Debayan GhoshA Coiled Tubing Perforating Solution Incorporating aGun Deployment System and Dynamic UnderbalanceTechnique Improves Well Production in High Angle DeepGas Wells in Saudi Arabia 23Hasan H. Al-Jubran, Jairo A. Leal Jauregui, Shaker A. Al-BuHassan,Simeon Bolarinwa, Wassim Kharrat, Dave Polson andMazen T. BarnawiFirst Borehole to Surface Electromagnetic Survey in KSA:Reservoir Mapping and Monitoring at a New Scale 36Dr. Alberto F. Marsala, Muhammad H. Al-Buali, Zaki A. Ali,Dr. Shouxiang M. Ma, Dr. Zhanxiang He, Tang Biyan, Guo Zhao andTiezhi HeToward Quantitative Remaining Oil Saturation (ROS):Determination Challenges and Techniques 46Ahmed A. Al-Harbi, Dr. Denis P. Schmitt and Dr. Shouxiang M. MaUtilizing Seismicly Derived Rock Properties for HorizontalDrilling in the Arabian Gulf 54Sean Rahati, Hussain M. Al-Otaibi and Yousef M. Al-ShobailiGenerating Seismicly-Derived High-Resolution RockProperties for Horizontal Drilling Optimization in theArabian Gulf 60Dr. J.A. Vargas-Guzmãn, William L. Weibel, Idam Mustika and QadriaAnbarPower of Inventions to Fuel Corporate Transformation 69Dr. M. Rashid KhanSaudi AramcoJournal of Technology

Innovative CT Abrasive Hydrajetting Perforating Approachin a Complex Saudi Arabian Gas Well Overcomes InitialInability to Perform a Proppant Fracturing Treatment andAchieves Better Than Expected Results: A Case HistoryAuthors: J. Ricardo Solares, Walter Nunez Garcia, Ataur R. Malik, Jose R. Vielma-Guillen, Alejandro Chacon and Craig WolfeABSTRACTOne of the key components for a successful hydraulic proppantfracturing treatment is an efficient perforating strategy,capable of ensuring good communication with the formationwhile reducing near wellbore pressure losses. It is not unusualto encounter unexpectedly high fracturing fluid injectionpressures and low injectivity conditions in wellsrequiring stimulation to increase their productivity. This isdue to a variety of reasons, including inadequate perforationtunnels. The problem gets compounded further in wellsdrilled in high pressure/high temperature formations, whereperforming proppant stimulation treatments poses a considerablechallenge.The general approach in wells selected for proppant fracturingtreatments is to use conventional perforating techniquesthat create relatively short tunnels with entry holeshaving a diameter several times larger than the mean proppantdiameter. Due to formation damage related effectscaused by drilling practices or other operational steps, longtunnels are frequently required to bypass the near wellboredamage and connect with the reservoir.Saudi Aramco has been successfully using hydrajettingtechnology for stimulating open hole horizontal wells. Yetthe application of the technique in vertical wells drilled insandstone reservoirs had been successfully tried only oncebefore its application in Well-A.This article describes how the inability to perform a proppantfracture treatment in a well drilled in a sandstone reservoir,which had originally been perforated using conventionalmethodology, was overcome by applying an innovative approachinvolving the use of hydrajetting technology. Theproppant fracture stimulation treatment had to be abortedafter very low initial injectivity and abnormally high stress atthe maximum allowable treating pressure were encounteredduring the DataFrac diagnostic test. After implementing theinnovative approach, a large size proppant fracturing treatmentwas successfully performed, improving well performancefrom a gas rate of 0 to 25 MMscfd at high flowing wellheadpressure (FWHP). The successful approach provided aviable cost-effective alternative to the conventional methodology,and it has since been applied with equally excellentresults in several additional cases.INTRODUCTIONPerforations have a great impact on the success of a fracturingtreatment as they affect how easily the fracture is generatedand the proppant is placed. Every perforation strategy needs tomeet several key objectives, such as guaranteeing an easybreakdown, minimizing near wellbore restriction and reducingtortuosity problems, to prevent proppant bridging or prematurescreen-out, which may result in the treatment’s not achievingits objectives. The treatment design itself — based ongeomechanical and other reservoir parameters — is critical tothe ability to pump the designed treatment through the perforations.Of particular importance are the entry hole diameterand hole penetration at downhole conditions 1-3 , and theorientation of the holes with respect to the fracture plane.In cased hole completions, shaped charge perforated holesoften create excessive localized stresses and can also slightlycrush the cement bond around the perforations. During afracturing treatment, these two conditions cause fractures toinitiate at locations away from the perforations, resulting invery high injection pressures and the creation of a tortuouspath near the wellbore behind the casing. Shaped chargeperforated holes are also known to reduce the permeability ofthe compacted zone around the perforated zone. Thehydrajetting (high-pressure water cutting) technique can beused as an alternative to conventional perforating guns incased wellbores. The technique eliminates localized stresses, sofracture initiation can be easily controlled. Lab testing andmodeling work have shown that creating long, large diametercavities in the formation can drastically reduce the fractureinitiation pressure and improve the ability to extend thefracture deeper into the reservoir 4, 5 , an issue of significantrelevance in tight formations.Hydrajetting was applied in gas producer Well-A after thewell had been perforated using conventional oriented perforatingguns and the DataFrac diagnostic test showed that the injectivityrequired to perform a scheduled proppant fracturingtreatment could not be achieved.WELL DESCRIPTIONA gas producer, Well-A, was drilled as an S-shaped and casedhole in a sandstone reservoir with a net pay of 260 ft and low2 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

StageRate(bpm)StageTime(min)StageVol(gal)StageVol(bbl)TotalVol(bbl)Fill the hole, establishinjection (treated and 15 25 15,540 370 370filtered 6% KCl)Maintain steadyinjection rate withwater (treated and15 3 1,890 45 415filtered 6% KCl)Shutdown, monitorpressure decline30With hole full, startmini falloff test (6%filtered KCl)Mini falloff test 15 3 1,890 45 460Shutdown, monitorpressure decline90With hole full, start SRT(6% KCl)Increase Rate 1 1 42 1 461Increase Rate 3 1 126 3 464Increase Rate 5 1 210 5 469Increase Rate 8 1 336 8 477Increase Rate 10 1 420 10 487Fig. 1. Well-A wellbore diagram.average porosity. The well was completed with a 5½” carbonsteel tubing string and a 7” liner top packer to a true verticaldepth (TVD) of 14,672 ft (14,895 ft measured depth (MD)).Figure 1 is the wellbore diagram.The well was perforated in the 14,575 ft to 14,605 ft MD,and a proppant fracturing treatment was planned with a targetrate of 20 MMscfd. The targeted reservoir exhibited a few layersof potentially unconsolidated formation, so a fracturing approachfor sand control — commonly applied by SaudiAramco throughout its gas development program, whereby indirectperforating in a consolidated zone away from the lowYoung’s Modulus zones is performed — was implemented.INITIAL PERFORATINGThe original plan was to orient perforated Well-A with 180°phasing, to align the fracture plane with the in-situ maximumhorizontal stress direction, in an effort to reduce the fractureinitiation pressure and the tortuosity derived friction pressure.There are effective and reliable methods to orient a perforatedS-shaped well. Therefore, it was perforated in an underbalancedcondition with 3 3 ⁄8” perforating guns at six shots per foot and60° phasing. The well was then flowed back for cleanup with a16/64” choke setting, but the flowing wellhead pressure(FWHP) dropped from 2,400 psi to 0 psi in 30 minutes. Thispressure drop highlighted the tight nature of the reservoir.Operations to perform a DataFrac diagnostic test were initiated,with plans to pump according to the schedule in Table 1.Increase Rate 25 1 1,050 25 512Increase Rate 40 1 1,680 40 552Step Down Test 35 0.17 245 6 558Step Down Test 30 0.17 210 5 563Step Down Test 25 0.17 175 4 567Step Down Test 15 0.17 105 3 570Shutdown, monitorpressure declineTable 1. DataFrac diagnostic test pumping schedule45 570Prepad (Linear Gel 20#) 20 2 1,680 40 610Minifrac (45# borate XLHPG)Minifrac (45# borate XLHPG)20 18 15,120 360 97040 9 14,280 340 1,310Flush (Linear Gel 20#) 40 9 15,120 360 1,670Shutdown, monitorpressure decline60 1,670Total 301 70,120 1,670While filling up the wellbore, the maximum allowable surfacetreatment pressure of 12,000 psi to maintain the completionintegrity was reached without actually initiating a fracture.Figure 2 shows the pressure response observed throughout thediagnostic test. Several additional attempts to establish the requiredinjectivity required to break down the formation andinitiate a fracture were unsuccessful. A total volume of 110barrels (bbl) of 6% KCl brine was displaced in the formationat a low injection rate while a rapid pressure decline was consistnetlyobserved after every shut-in period. Based on theseresults, the diagnostic test was canceled and a discussion aboutavailable options ensued.After evaluating several options, the decision was made toSAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 3

Fig. 2. Pressure data recorded throughout the pre-DataFrac diagnostic test.perform a hydrajetting operation using a 2” high-pressurecoiled tubing (HPCT) to try to overcome the drilling inducedstress, reduce any potential near wellbore pressure effect andimprove wellbore communication to the reservoir. The decisionto use hydrajetting was supported by the success and positiveresults achieved using the technique in carbonate reservoirs inthe same field 6 .HYDRAJETTING BACKGROUNDThe hydrajetting technique was first implemented back in thelate 1950s in an attempt to find a non-reservoir damaging approachto connecting with the reservoir, as an alternative toconventional perforating methods, which were known to createa damaged zone around the perforated area 7 . At the time,this approach took a long time when used and was relativelyimpractical, so its use was abandoned, and the approach wasnot tried again until several decades later. The technique hasgained momentum in recent times, and it is now commonlyutilized in a number of applications where it offers significant,distinct advantage over conventional perforating guns 6, 8, 9 ,including cost savings and in situations where transportingexplosives presents a significant logistical challenge.The most distinctive advantage offered by the technique inhydraulic fracturing operations is its ability to reduce nearwellbore tortuosity, thanks to the larger diameter holes generatedin comparison with the smaller diameter holes achievedwith conventional perforating guns. Reduction of near wellboretortuosity is important, because its presence often restrictshydrocarbon flow from the formation to the wellbore and restrictsinjection flow into the formation during hydraulic fracturingstimulation. Another significant advantage offered byhydrajetting over conventional perforating is that it eliminatesthe formation damage that it is created when the shaped chargesused in conventional perforation pierce the liner and the formation,creating debris that in many cases, plugs some or mostof the perforated tunnels. The debris generated during the hydrajettingoperation is removed with the abrasive slurry usedto break the formation, so the created slots are not plugged 4 .HYDRAJETTING OPERATIONDepth correlation was first performed using gamma ray/casingFig. 3. Details of depth correlation passes made in Well-A.Well-A Slotted Intervals14,555 ft14,557 ft14,560 ft14,563 ft14,575 ft14,578 ft14,580 ft14,585 ftTable 2. Well-A slotted intervals using the hydrajetting tool.collar locator memory gauges, making three passes to ascertainthe coiled tubing (CT) stretch that had to be accounted for inthe calculations. The first correlation pass was made with thepressure at 0 psi, the second with the pressure at 3,000 psi,and the last one with the pressure at 7,500 psi. The maximumCT stretch observed at the highest correlation pass pressurewas 3 ft. Details of the correlation passes are shown in Fig. 3.The eight intervals shown in Table 2 were then slotted over a30 ft gross zone, which partially overlapped with the initiallyperforated interval, using the 3.06” outer diameter jetting tool4 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Fig. 4. Jetting tool used in Well-A.with three 3/16” jet nozzles phased at 120°, Fig. 4, run on a 2”HPCT. The slots were created by pumping 100 bbl of sandladenslurry, comprising a 40 lb/gal concentration of linear gelwith a 0.5 lb/gal concentration of 20/40 proppant, at an averagepumping rate of 3.8 barrels per minute (bpm). The averageHPCT pumping pressure was 8,300 psi, which closely matchedthe pressure obtained from the simulation runs using HPCTsoftware. Figure 5 shows actual recorded data throughout thehydrajetting operation.A post-hydrajetting operation tag reached a maximumdepth of 14,572 ft, indicating that 13 ft of the slotted intervalwas covered with the proppant used as an abrasive material,so it was necessary to make an additional HPCT run to cleanout the borehole down to total depth. An injectivity test designedto ascertain the effectiveness of the hydrajetting operationwas then performed, Fig. 6. It was possible to achieve amaximum injection rate and surface pressure of 15 bpm and11,000 psi, respectively, a marked improvement over the maximuminjection rate and pressure of 4 bpm and 12,000 psi,respectively, achieved with conventional oriented perforatingtunnels before the hydrajetting operation. The instantaneousshut-in pressure (ISIP) recorded after the injectivity test was8,395 psi, which corresponded to a fracture gradient of 1.03psi/ft, thereby confirming the tight nature of the reservoir.DATAFRAC DIAGNOSTIC TEST RESULTSFig. 5. Actual data recorded throughout the hydrajetting operation in Well-A.Fig. 6. Actual pressure data recorded during the post-hydrajetting operationinjectivity test.A DataFrac diagnostic test, as per the pumping scheduleshown in Table 1, was successfully performed to calibrate fracturingparameters ahead of the scheduled proppant fracturingtreatment. The procedure comprised a mini falloff test, a stepup and down rate test, and a calibration injection test. A totalvolume of 182 bbl of 6% KCl brine was displaced into the formationduring the step rate test (SRT) at a maximum surfacetreatment rate and pressure of 40 bpm and 10,230 psi (15,720psi estimated bottom-hole pressure (BHP)), respectively. Dataanalysis yielded a fracture extension pressure of 13,810 psi at4.5 bpm. Figure 7 shows actual data recorded throughout thetest, and Fig. 8 shows results obtained during the SRT.A calibration test was also performed by displacing a totalof 210 bbl of 6% KCl brine and 720 bbl of 45# cross-linkedgel into the formation at a maximum surface treatment pressureand rate of 8,955 psig (14,395 psig estimated BHP) and40 bpm, respectively. The actual pressure response datarecorded throughout the test are shown in Fig. 9. The estimatedsurface ISIP was 6,075 psig, and the total friction pressurewith 20# linear gel at 40 bpm was approximately 2,200psi. The average near wellbore pressure loss was estimated at1,000 psi.Data analysis, Fig. 10, yielded an average estimated fractureclosure pressure of 10,800 psi, and an average fluid efficiencyof 35%. After closure, a pressure data analysis, Fig. 11,yielded estimated transmissibility of 3,050 md*ft/cp and averageformation permeability of 2 md. The calibrated fractureparameters indicated that no significant modifications to theFig. 7. Actual pressure data recorded throughout the DataFrac diagnostic test.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 5

Fig. 8. Results from the SRT performed in Well-A.Fig. 11. Post-closure pressure data analysis results.Fig. 9. Actual pressure response during calibration test.Fig. 10. Calibration test data analysis results.original treatment design were required, so plans were made toproceed with the implementation of the treatment.Analysis of the temperature log that was run in Well-A immediatelyafter the calibration test, Fig. 12, indicated that agross fracture height growth of approximately 130 ft (from14,480 ft to 14,610 ft) was achieved. The green bar in the logshows the hydrajetting slotted interval, and the red bar showsthe initially oriented perforated interval.PROPPANT FRACTURING TREATMENT DETAILSThe scheduled proppant fracturing treatment was successfullyperformed as per design by displacing a total volume of2,050 bbl of 45# gel pad, 211,400 lbs of 20/40 ISP and102,400 lbs of 16/30 resin coated proppant (RCP) proppantFig. 12. Post-calibration test temperature log run in Well-A.in stages ranging from 0.5 pounds of proppant added pergallon (PPA) to 9.5 PPA, displaced with 359 bbl and underdisplacedwith 3 bbl of 20# linear gel, into the formation atan average pumping rate of 40 bpm. Figure 13 shows the actualpressure response data, measured during the proppantfracturing treatment.The maximum surface rate and pressure achieved during thetreatment were 40 bpm and 10,230 psi (15,720 psi estimatedBHP), respectively. The net pressure achieved at the end of thetreatment was approximately 4,500 psi, and the estimated surfaceISP was 9,100 psig, a gain of 3,050 psi over the valuerecorded during the injection calibration test. Figure 14 shows6 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Fig. 13. Actual pressure data obtained during the proppant fracturing treatmentperformed in Well-A.the fracture geometry obtained from net pressure historymatch modeling.POST-FRACTURING TREATMENT RESULTSThe post-stimulation production test data, Table 3, clearly indicatesa highly successful treatment, as Well-A performed significantlybetter than anticipated. The performance of the wellimproved further as it continued to cleanup, and a month laterit was producing at an average rate of 25 MMscfd with aFWHP of 6,500 psi.Results from a pressure buildup and deliverability test performedfive months after the stimulation treatment, depicted inFig. 15, clearly show the bilinear flow signature of a fracturedwell. The test results analysis showed that a 300 ft half-lengthfracture with conductivity in excess of 7,000 md-ft was created.The maximum drawdown pressure reached throughoutthe deliverability test was only 300 psig. This figure comparesfavorably with the average drawdown pressure in excess of1,000 psi typically observed in offset wells after similar hydraulicfracturing treatment, therefore indicating the higherquality of treatment implemented in Well-A.Figure 16 shows the results from a nodal analysis comparingwell performance before and after the stimulation treatmentin Well-A, clearly indicating the significant wellperformance improvement achieved from the stimulationtreatment.CONCLUSIONS1. The hydrajetting technique proved to be a viable and costeffectiveoption in a gas producer that could not behydraulically fractured after being perforated usingconventional oriented perforating guns. The scheduledproppant fracturing treatment was successfully carried outFig. 14. Fracture geometry from history match modeling in Well-A.PrimaryChoke SizeFWHP(psig)BSW (%)Cl (ppm)H 2 S(ppm)CO 2(%)WHT (°F)Estimated Solids FreeGas Rate (MMscfd)30/64” 6,125 12 14,550 0 4 219 20Table 3. Post-stimulation test results in Well-ASAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 7

REFERENCESFig. 15. Results from post-stimulation pressure buildup test performed in Well-A.Fig. 16. Comparison of Well-A performance before and after the stimulationtreatment.after the hydrajetting operation, a treatment that otherwisewould had been aborted.2. The high fracture initiation pressure, observed during anattempt to perform a hydraulic proppant fracturingtreatment after the oriented well was perforated, wassignificantly reduced after hydrajetting.3. The high near wellbore pressure loss, observed during aSRT performed after the oriented well was perforated, wassignificantly reduced in a similar diagnostic test perfomedfollowing the hydrajetting operation.4. The final dynamic net pressure, obtained during thesuccessful hydraulic fracturing treatment, was slightlyhigher than 4,500 psi, which along with the tail-in RCPstage included in the treatment, resulted in a fast cleanoutand a high solids-free gas rate.5. The post-stimulation well performance, at a stabilized gasrate of 25 MMscfd with a FWHP of 6,500 psi, wassignificantly better than anticipated.Replacing sand — used as an abrasive material to help createslots during the hydrajetting operation — with acid solublematerial should be considered in future hydrajetting operationsto accelerate cleanout and eliminate the potential use of CT.ACKNOWLEDGMENTSThe authors would like to thank Saudi Aramco and Halliburtonmanagement for their permission to present andpublish this article. We would also like to thank EduardoSoriano for his contribution in evaluating post-stimulationwell performance.This article was presented at the SPE Annual TechnicalConference and Exhibition, Denver, Colorado, October 30 -November 2, 2011.1. Pongratz, R., von Gijtenbeek, K., Kontarev, R. andMcDaniel, B.W.: “Perforating for Fracturing – BestPractices and Case Histories,” SPE paper 105064,presented at the SPE Hydraulic Fracturing TechnologyConference, College Station, Texas, January 29-31, 2007.2. Ceccarelli, R.L., Pace, G., Casero, A., Ciuca, A. andTambini, M.: “Perforating for Fracturing: Theory vs.Field Experiences,” SPE paper 128270, presented at theSPE International Symposium and Exhibition onFormation Damage Control, Lafayette, Louisiana,February 10-12, 2010.3. Smith, C.G., Khiat, S. and Albadraoui, D.: “An EffectiveTechnique to Reduce Bottom-hole Friction Pressure duringHydraulic Fracturing Treatments,” SPE paper 112422,presented at the SPE International Symposium andExhibition on Formation Damage Control, Lafayette,Louisiana, February 13-15, 2008.4. Surjaatmadja, J.B., Abass, H.H. and Brumley, J.L.:“Elimination of Near-Wellbore Tortuosities by Means ofHydrojetting,” SPE paper 28761, presented at the SPE AsiaPacific Oil and Gas Conference, Melbourne, Australia,November 7-10, 1994.5. Solares, J.R., Amorocho, J.R., Bartko, K., et al.:“Successful Field Trial of a Novel Abrasive Jetting ToolDesigned to Create Large Diameter Long Cavities in theFormation to Enhance Stimulation Treatments,” SPE paper121794, presented at the SPE/ICoTA Coiled Tubing andWell Intervention Conference and Exhibition, TheWoodlands, Texas, March 31 - April 1, 2009.6. Nuñez-Garcia, W., Solares, J.R., Leal, J., et al.: “FirstSuccessful Low Cost Abrasive Perforation with WirelessAssisted Coiled Tubing in Deviated High Pressure/HighTemperature Gas Well,” SPE paper 136906, presented atthe International Petroleum Exhibition and Conference,Abu Dhabi, U.A.E., November 1-4, 2010.7. HydraJetTM Tool Data Manual, 7 th Edition, HalliburtonEnergy Services, 2006.8. McDaniel, B.W., Surjaatmadja, J.B. and East, L.E.: “Use ofHydrajet Perforating to Improve Fracturing Success SeesGlobal Expansion,” SPE paper 114695, presented at theCIPC/SPE Gas Technology Symposium 2008 JointConference, Calgary, Alberta, Canada, June 16-19, 2008.9. Rees, M.J., Khallad, A., Cheng, A., Rispler, K.A.,Surjaatmadja, J.B. and McDaniel, B.W.: “SuccessfulHydrajet Acid Squeeze and Multifracture Acid Treatmentsin Horizontal Open Holes Using Dynamic DiversionProcess and Downhole Mixing,” SPE paper 71692,presented at the SPE Annual Technology Conference andExhibition, New Orleans, Louisiana, September 30 -October 3, 2001.8 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

BIOGRAPHIESJ. Ricardo Solares was a PetroleumEngineering Senior Consultant and aSupervisor with the Southern AreaProduction Engineering Department(SAPED) in ‘Udhailiyah prior to hisrecent retirement. He has 27 years ofdiversified oil industry experience.Throughout his career, Ricardo held positions as aReservoir and Production Engineer with Arco Oil and Gasand BP Exploration, while working in the Middle East, theGulf of Mexico, Alaska and South America.After joining Saudi Aramco in 1999, he was involved witha variety of technical projects and planning activities thatare part of the company’s large gas development projects.His areas of expertise include hydraulic fracturing andwell stimulation, all aspects of production optimization,artificial lift design, pressure transient and inflow performanceanalysis, completions design and economic evaluation.In 1982, Ricardo received his B.S. degree in GeologicalEngineering, and in 1983, he received his M.S. degree inPetroleum Engineering, both from the University of Texas atAustin, Austin, TX. He also received an MBA in Financefrom Alaska Pacific University, Anchorage, AK, in 1990. Hehas also authored and coauthored over 45 SPE papers andarticles in a variety of local and international technicalpublications.Walter Nuñez Garcia is a ProductionEngineering Advisor at OXY,Occidental-Colombia, where he beganworking in June 2011. Previously, heworked as a Senior PetroleumEngineer for the Gas ProductionEngineering Division at Saudi Aramcofor almost 5 years. Walter has 18 years of overallexperience in the oil industry. Before joining Saudi Aramcoin 2006, Walter worked for ECOPETROL (the Colombiannational oil company), serving in several different technicaland administrative positions.In 1992, he received his B.S. degree in PetroleumEngineering from the Universidad America, Bogota,Colombia, and in 2000, he earned a Finance degree fromLa Gran Colombia University, Bogota, Colombia. Walterthen went on to earn his M.S. degree in PetroleumEngineering from the University of Oklahoma, Norman,OK, in 2002.He is a member of the Society of Petroleum Engineers (SPE).Ataur R. Malik is a Senior PetroleumEngineer who joined the GasProduction Engineering Division ofSaudi Aramco in 2008. Prior tojoining Saudi Aramco, he began hiscareer with Enviro-Test Laboratories,Canada, in 1998, and then joinedSchlumberger Canada Ltd. in 2000, working as a WellStimulation Specialist in Canada and offshore Malaysia.In 1995, Ataur received his B.S. degree in ChemicalEngineering from Washington State University, Pullman,WA, and his M.E. degree in Chemical Engineering fromCity College of the City University of New York, NewYork, NY, in 1998.He is registered as a Professional Engineer with theAssociation of Professional Engineers, Geologists, andGeophysicists of Alberta (APEGGA), Canada, and is amember of the Society of Petroleum Engineers (SPE).Jose R. Vielma-Guillen is theHalliburton Technical Advisorsupporting coiled tubing (CT) andstimulation operations for theSouthern Area Production EngineeringDepartment (SAPED). He has 20 yearsof experience in the oil and gasindustry in several areas, including workovers and CToperations, matrix acidizing, hydraulic fracturing,perforating, sand control, well cementing, water shut-offand production surveillance. In 1990, he received his B.S.degree in Civil Engineering from the Instituto UniversitarioPolitécnico de las Fuerzas Armadas Nacionales (IUPFAN)and in Metallurgical Engineering from the UniversidadCentral de Venezuela (UCV), Caracas, Venezuela.Jose is a member of the Society of Petroleum Engineers(SPE) and has authored several technical papers on fieldtechnology applications, fluids and stimulation results.Alejandro Chacon is the LeadTechnical Engineer for HalliburtonCoiled Tubing in Saudi Arabia. He hasheld this position since January 2009.Alejandro joined the industry in early2006 in Colombia as a Field Engineer,and since then he has gained experiencein the following types of operations, among others: matrixstimulation, pinpoint stimulation, logging, CT-TCP,conformance and general coiled tubing (CT) extendedreach applications.He is currently focusing on new technology applicationsfor CT operations in Saudi Arabia.In 2006, Alejandro received his B.S. degree inMechanical Engineering from the Universidad de los Andes,Bogota, Colombia.Craig Wolfe is a Field Engineer inHalliburton’s Production EnhancementDepartment. He has 6 years ofexperience in the energy industry.During that time, Craig has worked inSaudi Arabia, Egypt and severallocations across the United States. Hisexperience is mainly in proppant and acid fracturetreatments, but he has also worked in Halliburton’sCement Department.In 2005, Craig received his B.S. degree in GeosystemsEngineering and Hydrogeology from the University ofTexas at Austin, Austin TX.He is an active member of the Society of PetroleumEngineers (SPE).SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 9

A Novel Enzyme Breaker for Mud CakeRemoval in High Temperature Horizontal andMultilateral WellsAuthors: Dr. Mohammed H. Al-Khaldi, Dr. Bisweswar Ghosh and Debayan GhoshABSTRACTEffective removal of the drilling mud filter cake during wellcompletion is essential to reduce the formation damage causedby drilling activities in many production and injection wells.This task is very difficult to achieve, especially in horizontal/multilateral wells. Harsh chemical treatments (acids, oxidizersand chelating agents) have been used extensively to conductwater-based mud cake cleanup treatments; however, these approacheshave been limited due to the associated high corrosionrates and uneven mud cake removal. With their controlledreaction with the mud cake, mild chemical nature, and betterhealth, safety and environmental profile, enzymes provide anexcellent alternative to harsh chemical treatments in high temperatureformations. Subsequently, their use has been limitedto relatively low temperature applications due to their instabilityat elevated temperatures.In this work, two enzymatic systems were evaluated: Theold a-amylase system and a new structurally reinforced a-Helixsystem. The old enzyme was found to form a potentiallydamaging precipitate at reservoir temperatures above 100 °C.The degree of this damage was assessed using a size matchingtechnique and coreflood experiments. This potential of secondaryformation damage was drastically reduced in the new improvedenzyme system. Enzyme denaturing here was minimizedby protecting the catalytic center using preferentialhydration of proteins with a polyol.The effectiveness of the new system was proven in the labthrough comparative tests. A bioassay by reducing sugar estimationshowed the better biopolymer hydrolyzing capability ofthe new system at higher temperatures in contrast to the oldenzyme system, and coreflood experiments, conducted at hightemperatures using the new enzyme system, showed that enzymedenaturing did not occur and the core oil permeabilityincreased at a stabilized pressure. In addition, this article willhighlight the advantages and disadvantages of each enzymesystem in terms of stability, compatibility and mud cake damagereversal.INTRODUCTIONWater-based drill-in fluids (DIFs) are commonly used indrilling the pay zone of many horizontal/multilateral wells.These fluids are typically designed by incorporating xanthan,starch or polyanionic cellulose with bridging agents, such assized calcium carbonate particulates. Compared to regularmud systems, DIFs are relatively clean and cause minimum formationdamage to the target zone. They are engineered to forma coating (mud cake) on the borehole wall in permeable formationsas a result of filtration into the rock pores. The formedmud cake will seal the wellbore and prevent both the fluid filtrateand the drill solids from invading and damaging the payzone. Once drilling activities are completed, effective removalof the formed mud cake, which has created an impermeablebarrier on the wellbore wall, is essential to restore well productivityor injectivity, prevent completion equipment failures andreduce drilling associated skin factors.Several methods have been used to clean up water-basedmud filter cakes. These include hydrochloric (HCl) acid, weakorganic acid systems, chelating agents, oxidizing breakers (peroxides)and delayed-release acids 1-5 . These approaches areused to dissolve the bridging materials in the mud cake, i.e.,calcium carbonate particles. Although the use of these harshchemicals, such as HCl acid, is widespread there are manyconcerns and limitations associated with their use, especially inhigh temperature horizontal wells. Strong acids have high corrosionrates, which are difficult to inhibit at high temperatures.In addition, their high reaction rates with calcium carbonateresult in the degradation of the filter cake at the point of acidintroduction, which promotes a rapid and localized reactionand results in uneven removal of the filter cake, leaving the remaininghorizontal section untreated.Over the past decade, enzymes have been used to overcomedrawbacks associated with the use of conventional mud cakeremoval treatments 6-8 . Their controlled reaction with the mudcake, mild chemical nature, and better health, safety and environmentalprofile make them an excellent alternative to acidicsystems. Typically, a-amylase enzymes are used to break downthe polymer components of the filter cake: xanthan and starch.Their gradual action on the xanthan and starch polymers enablesthe enzymes to be placed across the entire horizontal sectionbefore the start of the reaction so they attack the formedmud cake in a uniform way. Weak organic acids can also be incorporatedinto enzyme solutions to enhance their mud cakeremoval ability 9, 10 . This combination of enzymes and organicacids results in a dual attack on both the calcium carbonateand the polymer components of the filter cake. Consequently,10 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

the main limitations of enzymes are their instability (denaturing)at elevated temperatures, inactivity in certain carrier fluidsand potential to cause formation damage due to coagulation(precipitation), particularly at high salinity and high temperatures.Historically, enzymes were considered to be a mediocreagent because of their pH, temperature and brine tolerancelimitations. Therefore, there has been a need for more thermophilicand thermostable amylase enzymes 8, 11, 12 .In this article, different conventional a-amylase enzymes (A,B and C) and new, modified a-amylase enzymes (D, E and F)are evaluated. The new enzyme systems were modified bystructurally reinforcing the a-Helix layer and protecting thecatalytic center. The modification was achieved using the preferentialprotein hydration technique, where polyols are used asa co-solvent with water to enhance the enzyme stability. Theformation damage potential and enzyme inactivationm (due todenaturing) of both the conventional and the new enzyme systemswere investigated at temperatures above 200 °F usingBradford assay, starch degradation and coreflooding techniques.THEORYThe a-amylase enzymes comprise a glycoside hydrolyses proteincomplex that contains a linear chain of amino acids. Theseenzymes fold to create a 3D structure with active sites, Fig.1 13 , that catalyzes the hydrolysis of the internal a-1, fourbonds in large polysaccharides molecules, such as starch andglycogen, yielding soluble maltodextrins, glucose and maltose.Being a catalyst, the enzymes speed up the hydrolysis of starchpolymer in water by lowering the activation energy of thehydrolysis reaction. In addition, they remain unaltered by thecompleted reaction and continue to function with infinite reactivity;however, different environmental factors can damage theenzyme (denaturing reaction), and as a result it loses its activity.The two main environmental factors that affect the enzymeactivity are pH and temperature 14-17 .Although a-amylase enzymes have been successfully used toremove wellbore damage caused by filter cake, their applicationshave been limited to relatively low temperature and highpH treatments 9, 10, 18, 19 . Their activity and stability are a functionof both the pH and the temperature of the reaction solutions.Like most chemical reactions, the rate of an a-amylaseenzyme catalyzed reaction with starch or xanthan increases asthe temperature is raised. An increase in reaction temperatureas small as 1° or 2° may introduce changes of 10% to 20% inreaction rate; however, this improved rate is complicated bythe fact that many enzymes are adversely affected by high temperatures.Figure 2 shows that the enzyme catalzed reactionrate increases with higher temperatures to a maximum level,then abruptly declines with any further increase in temperature.Depending on its own origin, each a-amylase enzyme hasits own optimum temperature range. For example, it was reported15 that a-amylase enzymes produced from Heliodiaptomushave optimum activity at 30 °C. The enzyme became inactiveat 60 °C and 70 °C after 2 hours and 1 hour, respectively.Fig. 2. Temperature effect on enzyme stability and activity.Fig. 1. 3D structure of a typical α-amylase enzyme 13 .Fig. 3. pH effect on enzyme stability and activity.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 11

a-amylase enzymes produced from the Bacillus species aremore thermally stable. It was found 16 that the optimum temperaturefor those enzymes is 40 °C and they retained their potencyeven at 70 °C. Several other studies have shown that a -amylase enzymes, when subjected to elevated temperature,could become irreversibly inactive coagulations 6, 11 .Besides temperature, the pH value of the solution has a significanteffect on the enzyme stability and activity. The most favorablepH value, the point where the enzyme is most active,is known as the optimum pH, Fig. 3. Extremely high or lowpH values generally result in a complete loss of activity formost a-amylase enzymes. The pH value is also a factor in thestability of an enzyme. As with activity, for each enzyme thereis also a region of pH optimal stability. Many studies have reportedthat a-amylase enzymes are most active at a pH rangeof 3-5 20, 21 . At higher pH values, the tested enzymes became inactive.Some enzymes became active again at optimum pH values,while other enzyme’s inactivity was irreversible, indicatingthat the enzymes had became denatured. Similarly, at low pHvalues, less than 3, the enzyme activity decreases and the enzymemight become denatured 14, 15 .One of the main disadvantages of most enzyme systems istheir relatively low stability. As stated above, they can be chemicallydenatured if applied in nonoptimum pH and temperatureranges. Therefore, to correctly apply enzyme systems in mudcake removal treatments, their temperature and pH dependencemust first be understood.EXPERIMENTAL STUDIESMaterialsThe activity and thermal stability of the conventional and newenzyme systems were studied at temperatures above 200 °F. Alltested enzyme breaker formulations (5 vol% enzyme, 2 wt%KCl brine) were prepared using distilled water with a resistivitygreater than 18 M.cm at 25 °C. KCl salt was supplied byFisher Scientific Co. (Analytical Reagent) with an assay valueof 99.7%. Starch, used for the starch degradation test, was atypical field sample and was used without further purification.The iodine reagent was prepared using iodine solution Merck1.09.099.1000 and potassium iodide (KI) Merck 1.05043.0250PA. Table 1 shows the chemical composition of the mud sampleused during the coreflood experiments.Enzyme Thermal Stability and InactivationThe thermal stability of different enzyme systems was assessedat a temperature of 212 °F. These experiments were carried outusing high-pressure/high temperature (HP/HT) see-throughcells where nearly 100 cm 3 of the enzyme solution was placedin a glass tube and equilibrated at the desired temperature undera pressure of 300 psi. Each experiment was run for a periodof 2-16 hours. During the incubation period, the equilibratedenzyme solution at the desired temperature was visuallyobserved for any physical change or coagulation formation. AtComponentthe end of the soaking time, any solid precipitation or coagulationthat was present in the mixture solution was collected using1.2 µm filter paper for analysis with a scanning electronmicroscope (SEM). Aliquots of fluid samples were collected atdifferent periods for inactivation tests and pH/Ca analysis.Starch degradation using the starch/iodine test was used toinvestigate the effect of exposure time at high temperatures onthe enzyme activity. The inactivation experiments were conductedusing the aliquots collected at different time intervalsduring the denaturing tests, which were conducted at 212 °F.In each experiment, 1 ml of 5 wt% starch solution was mixedwith 1 ml of a specific enzyme aliquot collected after a certainexposure time. The pH value of this mixture was raised to 10by adding diluted NaOH in successive drops. Then the mixturewas incubated for 5-10 minutes in a small plastic boat beforethe addition of the iodine solution. A control experimentwas conducted using a starch solution only, without any enzymealiquot.Protein Estimation Assay: Bradford AssayConcentration, g/LDrilling Grade Xanthan 4.2Starch 14.2PAC 1Sodium Hydroxide 0.8Potassium Chloride 50CaCO 3 (10 micron) 100CaCO 3 (25 micron) 100Table 1. Chemical composition of drilling mud used during coreflood experimentsThis test was conducted to determine the concentration ofnon-denatured protein in solution following incubation at hightemperatures. The procedure is based on the formation of acomplex between the dye, Brilliant Blue G, and structural proteinsin solution. The formation of the protein dye complexcauses a shift in the absorption maximum of the dye from 465nm to 595 nm. The amount of absorption at 595 nm is proportionalto the amount of the protein present. The proteinconcentration can be easily found from the standard curve developedusing the Bradford Reagent and known concentrationsof standard protein assays.The standard curve was developed using the BradfordReagent (Brilliant Blue G in phosphoric acid and methanol),and Bovine Serum Albumin (BSA), a standard protein solutionwith a range of 0.1 mg/ml to 1.4 mg/ml. Five different BSA solutionswere prepared so that they had a protein concentrationof 0, 0.25, 0.5, 1.0 and 1.4 mg/ml. Each one of these solutionswas mixed with the Bradford Reagent, so that each solutionconsisted of 0.1 ml of a protein sample and 3 ml of thereagent. After mixing, absorbance was measured at 595 nmwithout delay as the protein dye complex is stable only up to12 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Fig. 4. Protein concentration standard curve.60 minutes. Absorbance of the samples was recorded beforethe 60 minute time limit and within 10 minutes of each other.Using the generated standard curve, Fig. 4, the concentrationof non-denatured protein, after heat shock, in the conventionaland new amylase enzymes was determined. In certain cases, theenzyme solution was diluted so that the protein concentrationwould fall within a detection limit of 0.1 mg/ml to 1.4 mg/ml.Coreflood TestingCoreflood testing was conducted to investigate the effect of enzymeinstability at high temperatures on the permeability ofdifferent core plugs, which were cut from one piece of coreblock parallel to the bedding plane of sandstone in field X. Initially,these core plugs were cleaned by a submerged solventcleaning technique and then dried overnight at 60 °C. Theporosity value for each core was determined using Boyle’s law,using helium, and the air permeability value was measured usingDarcy’s law at 200 psi, Table 2. The coreflood experimentswere carried out in a linear mode at a temperatures of 200 °Fto 295 °F, a back pressure of 550 psi and an overburden pressureof 2,500 psi.In the first conducted coreflood experiment, the core plugwas first saturated with 5 wt% KCl while monitoring the pressuredrop across the core plug. Initial core permeability to 5wt% KCl was determined at 1 cm 3 /min. This step was followedby the injection of nearly 2 pore volume (PV) of the injectionof 5 vol% enzyme-A solution at 1 cm 3 /min. Then, thesaturated core plug with the enzyme solution was incubated at200 °F for 2 hours. After incubation, 5 wt% KCl brine was injectedagain to measure the final core plug permeability.In another set of experiments, the old and new enzyme system’sremoval efficiency was investigated. For each enzymesystem, a fresh core plug was used to conduct the coreflood experiment.The extent of damage due to drilling mud and thecleaning efficiency of the enzyme systems were evaluatedthrough a series of comparative coreflood experiments at a targetedreservoir condition of 245 °F. The three main steps of thecoreflood experiments were:Step 1. Filter Cake Buildup: The wellbore side of the core facewas exposed to drilling fluid in dynamic circulation for 12hours at 10 cm 3 /min, followed by static exposure for 12 hoursat 700 psi overbalance.Step 2. Mud Displacement: The remaining mud in the flowlines and across the wellbore face of the core sample was displacedby flowing 2 liters of injection water at 700 psi overbalance,at a flow rate of 200 cm 3 /min.Step 3. Enzyme Treatment: The enzyme solution was flowedacross the core face at 10 cm 3 /min and then soaked for 6 hoursbefore being flushed with injection water.Step 4. The oil return permeability measurement was carriedout with crude oil from the reservoir side before the core plugwas offloaded for visual observation.Test No. Treatment Porosity (%) Base Ko (mD) Return Ko (mD) Results1Enzyme-A(5% solution)16.1 3.96 0.812Enzyme injected into coreplug and soaked for 2 hours.Core permeability decreasedby 80%.2Enzyme-B(5% solution)23.48 134.3 73.245.5% permeability damage.The denatured protein couldbe found on the face of thecore plug.3Enzyme-T(5% solution)23.98 138.7 124.210.4% permeability damage.The core face is almost clean.The damage could be internal.4Enzyme-R1(5% solution)23 123.9 101.518% permeability damage.A thin layer of denaturedprotein could be seen on theface of the core plug.Table 2. Petrophysical properties of core plugs and enzyme treatment resultsSAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 13

Photo 2. Compatibility problems between neat bio-acetic acid and α-amylase enzymeat room temperature.Photo 1. Thermal stability of both bio-acetic acid and α-amylase enzyme-A at 212 °F.Analytical TechniquesThe calcium concentration was measured using inductivitycoupled plasma. Density measurements were made with a Paardigital density meter (DMA 35N). To measure the pH value ofthe collected aliquots of various enzyme formulations, anOrion model 250A meter and Cole-Parmer Ag/AgCl singlejunction pH electrode were used. Particle size distribution wasmeasured using a Malvern Mastersizer 2000 analyzer, whichhas a measuring range of 0.02 µm to 2,000 µm.RESULTS AND DISCUSSIONSThermal Stability and InactivationThe thermal stability of the collected a-amylase enzyme systemsamples was examined using HP/HT see-through cells at212 °F. The neat a-amylase enzyme-A precipitated, in the formof coagulation, at 212 °F over a time period of 2-3 hours,Photo 1. Similarly, this enzyme coagulated instantaneouslywhen it was mixed with bio-acetic acid at room temperature,Photo 2. This behavior demonstrated that this a-amylase enzymeis not stable at high temperatures (212 °F) and low pHvalues (1 to 2). Compared to temperature effect, the denaturingreaction due to low pH shock was faster, indicating the likelihoodthat the produced denatured enzymes are different. Thispossibility was confirmed from SEM analysis conducted on thecollected samples, Photo 3. The enzyme denaturing reactiondue to temperature effect resulted in a coagulation with somestructure, which was absent in the denatured enzyme at lowpH values; however, both coagulated enzymes formed an im-Photo 3. SEM analysis of different denatured enzyme samples at different conditions.14 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Formulation Component Concentration Mixing Order12DistilledWaterKCl Enzyme-ABio-aceticAcidDistilledWaterEnzyme-ABio-aceticAcidAs required tomake upvolume(200 ml)2 wt%5 vol%3 vol%As required tomake upvolume(200 ml)5 vol%3 vol%EnzymeBio-aceticAcidEnzymeBio-aceticAcid34DistilledWaterEnzyme-ABio-aceticAcidDistilledWaterEnzyme-AAs required tomake upvolume(200 ml)5 vol%3 vol%As required tomake upvolume(200 ml)5 vol%Bio-aceticAcidEnzyme-Table 3. Composition of different enzyme-A formulationspermeable precipitation, which might add another barrier onthe wellbore wall during field applications. Initial laboratoryexperiments revealed the neat enzyme component instability atreservoir temperatures and its compatibility problems with thebio-acetic acid. The remaining laboratory studies focused oninvestigating the enzyme system stability when it is used at dilutedconcentrations. The effects of temperature, pH, brine andmixing order on enzyme-A’s system stability were studied indetail using several formulations, Table 3. Formulation 1 containeda-amylase enzyme-A, bio-acetic acid and KCl salt at 5vol%, 3 wt% and 2 wt%, respectively. Similar to what occurredwith the neat enzyme components, this cleanup fluidformed coagulation immediately when bio-acetic acid wasadded to the a-amylase enzyme and KCl solution mixture.Further precipitation occurred when the final cleanup solutionwas exposed to high temperature (212 °F) for 2 hours, Photo4. This behavior was also observed with the other cleanup formulationsprepared using distilled water. It was interesting tonote that the order of mixing the two enzyme system componentsaffected the denaturing reaction rate. The formation ofenzyme coagulation was faster when the bio-acetic acid wasthe last component added to the cleanup solution. Precipitationoccurred after nearly 15 minutes compared to only 1minute when bio-acetic acid was added before the enzymecomponent, compared to only 1 minute when it was added after,Photos 5 and 6, respectively. In both cases, further precipitationtook place at elevated temperatures. These resultsclearly indicated that the presence of acetic acid resulted in theenzyme instability due to low pH shock; however, the a-amy-Photo 4. Precipitation in diluted mixtures of enzyme-A with bio-acetic acid at 5 vol%enzyme and 2 vol% bio-acetic acid in 2 wt% KCl brine.Photo 5. Precipitation in diluted mixtures of enzyme-A with bio-acetic acid at 5 vol%enzyme and 2 vol% acetic acid (in preparing the mixture solution, enzyme was thelast component added).SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 15

Photo 6. Precipitation in diluted mixtures of enzyme-A with bio-acetic acid, at 5 vol%enzyme and 2 vol% acetic acid (in preparing the mixture solution, bio-acetic acid wasthe last component added).Photo 7. Precipitation in diluted solutions of enzyme-A at 5 vol% enzyme at 212 °F.lase enzyme could be used alone to dissolve water-based mudcake without the presence of bio-acetic acid. Therefore, thethermal stability of the diluted enzyme solution was tested at212 °F. Although the enzyme coagulated after an exposuretime of 2 hours, the amount of precipitation was minimalwhen compared to that formed in the presence of bio-aceticacid, Photo 7.This difference in precipitation was also concluded from theenzyme activity tests. Starch degradation tests were conductedusing aliquots collected during the thermal stability experiments.In each experiment, 1 ml of 5 wt% starch solution wasmixed with 1 ml of a specific enzyme aliquot, collected after acertain exposure time at 212 °F. A control experiment wasconducted using only a starch solution without the presence ofan enzyme aliquot. A positive starch/iodine test (with bluecolor) indicated the presence of starch. In Photo 8, a blue colorwas observed for sample 1, the control experiment with no enzymepresent. Similarly, a blue color developed for sample 2,which has aliquot of enzyme/bio-acetic acid mixture withstarch; this indicated the inactivity of the enzyme. In contrast,negative starch/iodine tests were noted for samples 3 to 8 containingincubated diluted enzyme aliquots for 1, 2, 4, 6, 8 and16 hours at 212 °F. This demonstrated that the enzyme wasstill active even after 16 hours exposure at an elevated temperature.Although coagulation occurred during the thermal stabilitytests, the enzyme solutions still had sufficient catalyticamounts for the cleanup of starch.In another set of experiments, conventional and new, structurallyreinforced a-Helix enzyme samples were tested to visuallyobserve their thermal degradation with time at 245 °F. EnzymesA, B and C were from the unimproved conventionalenzyme range while enzymes D, E and F were Stage-1 samplesdeveloped to reduce protein denaturation and precipitation.Nearly 200 ml samples (5% enzyme solution in 2% KCl brine)of enzymes A, B, C, D, E and F were taken in high strengthbottles and digested in an autoclave at 245 °F for 6 hours. Theautoclave was programmed for 2 hour runs at one stretch.After every 2 hours, the samples were taken out and photographedto assess the extent of protein denaturation, Photos 9to 12. It was observed that all the samples became more or lesscloudy after 2 hours of digestion. After 4 hours, coagulationand phase separation could be clearly observed in enzyme samplesA, B and C, while D, E and F showed less coagulation andprecipitation. After 6 hours of digestion, it was evident fromvisual observation that not many further changes had occurredin terms of phase separation and precipitation. The sample Dremained cloudy with the least amount of precipitated particles.Enzymes R, R-1 and R-2 were developed as modified proteinvariants from the heat precipitating enzyme-E (of Stage-1)and T, T-1 and T-2 were developed by protein modification ofenzyme D (of Stage-1) to further minimize heat denaturation athigh temperatures. The thermal stability of these enzymes wasinvestigated at 245 °F using a 5% solution of enzymes in a 2%KCl solution. Photos 13 to 16 show the denaturation of these16 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Photo 13. The enzyme (Stage-2) solutions in distilled water before digestion.Photo 8. Starch degradation test of various aliquots collected from enzymatic-Asolutions incubated for different time intervals at 212 °F.Photo 14. The enzyme (Stage-2) solutions after 2 hours of digestion at 245 °F.Photo 9. The enzyme (Stage-1) solutions in distilled water before digestion.Photo 15. The enzyme (Stage-2) solutions after 4 hours of digestion at 245 °F.Photo 10. The enzyme (Stage-1) solutions after 2 hours of digestion at 245 °F.Photo 16. The enzyme (Stage-2) solutions after 6 hours of digestion at 245 °F.Photo 11. The enzyme (Stage-1) solutions after 4 hours of digestion at 245 °F.Photo 12. The enzyme (Stage-1) solutions after 6 hours of digestion at 245 °F.Fig. 5. Precipitation amount in different enzyme solutions after incubation at 245 °Ffor 6 hours.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 17

Fig. 6. Un-denatured protein curve showing sample T as the best performer.Fig. 9. Effect of denatured enzyme-A on core plug permeability. Enzyme-A solutionwas soaked for 2 hours at 212 °F.tions using the Bradford assay. As expected from the thermalstability testing, enzyme T showed the least protein denaturationafter heat shock, where it had the highest protein concentrationafter 1 hour of incubation at 245 °F.Potential Formation Damage due to Enzyme CoagulationFig. 7. Fractional permeability of Arab-D cores as a function of pore throat or particlesize diameter.Fig. 8. Particle size distribution of suspended particles in denatured enzyme-Asolutions after exposure time of 2 hours at 212 °F.Many researchers have reported that the suspended solids ininjected fluids should be minimized because they can cause severeformation damage in tight formations. Suspended solidscan impair the formation by various damaging mechanisms,such as wellbore narrowing by building external filter cake,pore throats plugging by solids invasion, and wellbore fill-upby particles settling. The effect of solids on the permeabilityaround the critical near wellbore area has been investigated 22-25 .They reported that wells with fractures (high permeability) cantolerate solids more than the wells with low permeability valueswhen produced water was re-injected into the matrix. Rajuet al. (2005) 22 investigated the potential formation damagethat can result from injecting commingled aquifer water andgas-oil separation plant disposal water into tight carbonatereservoirs. Their results revealed that potential damage wasconstrained by the core’s initial permeability. Although commingledwater damaged cores with an initial permeability ofless than 20 mD after injecting 1,000 PV, there was no damageobserved in cores with a permeability greater than 60 mD.One approach to determining the potential and type of formationdamage due to suspended particles in injected fluids is thematching technique. In this method, size matching between thecharacteristic particle size of suspended particles in injectedfluids and the characteristic pore throat size of the formation isused to find the minimum particle size that would not causeany permeability impairment. The correlations 26, 27 , known asthe “one-seventh” and “one-tenth” rule, respectively, state thatparticles with sizes between one-seventh and one-tenth of themedian pore throat diameter can cause internal plugging. Onthe other hand, it has been reported 28 statistically that externalplugging occurs at a particle/pore throat ratio of 0.45 orabove. Figure 7 shows the results of a study conducted on oneof the core plugs, with the measured pore throat diameter andcalculated damaging particle sizes based on different correlaenzymesolutions. It could be seen that some of the sampleswere distinctly less cloudy after various digestion stages, whencompared to Stage-1 modified enzymes. After the first 2 hours,all bottles started to develop various levels of haze, and after 4hours of digestion, some bottles started showing signs of coagulationand phase separation, except enzyme samples R-1 andT. After 6 hours of digestion, it was evident from visual observationthat not much further change had occurred in terms ofphase separation and precipitation; the samples R-1 and T remainedless cloudy and appeared to have the least amount ofprecipitated particles among all, Fig. 5. Figure 6 shows theconcentration of non-denatured protein in the enzyme solu-18 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Fig. 10. Coreflood experiment conducted using enzyme-B.Fig. 11. Coreflood experiment conducted using enzyme-T.tions as a function of fractional permeability (ratio of the permeabilityof an impaired core to the original permeability). Forexample, a 30% loss in permeability, or a fraction permeabilityof 0.7, would result from injecting fluids that contain particleswith diameters > 8 µm. These particles would plug all accessiblepore throats that have diameters of < 17 µm. Particles withdiameters less than 3 µm would not cause any permeability impairment,as indicated by the one-seventh and one-tenth rule.Figure 8 shows the particle size distribution of different enzyme-Asolutions after an exposure time of 2 hours at 212 °F.These solutions were prepared using a -amylase enzyme-A andwater at 5 vol% and 95 vol%, respectively. In some solutions,calcium chloride was added at 1 wt%, 3 wt% and 5 wt% toinvestigate the effect of calcium on the enzyme stability. FromFig. 8, it is clear that the enzyme in all solutions coagulatedand formed different precipitates with various particle size distributions.In general, the presence of calcium ions resulted inenzyme precipitates with smaller particle sizes. For example,enzyme precipitation in solutions containing 500 ppm calciumhad an average particle size of nearly 53 µm compared to 490µm with no calcium present. These precipitations would resultin permeability impairment if they were introduced into theformation during mud cake removal treatments. For example,based on Fig. 7, the a-amylase enzyme fluid containing 500ppm calcium would form precipitates with particle sizes rangingfrom nearly 4 µm to 100 µm. These suspended particlescould cause both internal and external formation plugging.This was confirmed using coreflood experiments, Fig. 9. It isclear that when enzyme-A was soaked in a core plug with aninitial permeability of 3.9 mD, it resulted in nearly 80% permeabilityreduction.The results of core flow tests and the permeability comparisonbefore and after mud removal treatment with various enzymesare shown in Figs. 10 to 12. The coreflood results showthat the conventional enzyme (Enzyme-B) severely damagedthe core plug due to secondary precipitation of sticky denaturedprotein. The denatured materials could be seen on thecore face after offloading. In comparison, the improved enzymesperformed satisfactorily within an acceptable limit.Among the two improved enzymes tested, enzyme-T had betterbiopolymer cleaning efficiency, with a 90% return permeability,and on visual examination, no secondary damage could befound.CONCLUSIONSFig. 12. Coreflood experiment conducted using enzyme-R-1.The thermal stability and activity of various enzyme systemswas investigated at different conditions, such as temperature,pH and mixing brine, using HP/HT see-through cells andstarch degradation testing. The following main conclusionswere drawn from the analyses conducted on the results ofthese experiments:• a-amylase enzymes and acids are incompatible. Theyformed coagulation when they were mixed at roomtemperature using neat or diluted concentrations.• The cleanup formulations containing both the a-amylaseenzyme and bio-acetic acid formed further precipitationwhen they were exposed to elevated temperatures, 212 °F,for 2 hours.• The use of brine solutions KCl and CaCl 2 did not preventthe precipitation in enzymatic solutions of a-amylaseenzyme and bio-acetic acid.• Neat and diluted solutions of a-amylase enzyme alonewere stable at room temperature; however, at 212 °F, theySAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 19

formed precipitations after an incubation time of 2 hours.• Once precipitated, mixtures of the a -amylase enzyme andbio-acetic acid were ineffective. Solutions of a -amylaseenzyme alone degraded starch even after an exposure timeof 16 hours at 212 °F.• Precipitated particles in different enzyme solutions caninduce damage due to formation plugging.The new enzyme system, developed by protein engineeringtools, has been proven in the lab through several comparativebiological and petroleum engineering tests to produce less denaturedprotein damage when subjected to the high temperaturestypical of downhole conditions in the region. In the kineticcompetition of Biopolymer 1,4-a-D-glucan hydrolysis bythe enzyme and simultaneous heat disruption of the hydrogenbonds to uncoil the alpha-helix and beta sheets, the new enzyme-T,referred to in this article, demonstrated the higheststability, resulting in the least denaturation and maximumreturn permeability.ACKNOWLEDGMENTSThe authors would like to thank Saudi Aramco managementfor granting permission to publish this article and also for providingthe resources to conduct all enzyme-A testing. Thanksalso go to both the Petroleum Institute and EPYGEN forgranting permission to publish this article and also for providingthe resources to conduct all the new modified enzyme evaluations.This article was presented at the SPE Asia Pacific Oil andGas Conference and Exhibition, Jakarta, Indonesia, September20-22, 2011.REFERENCES1. 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Nasr-El-Din, H.A., Al-Otaibi, M.B., Al-Qahtani, A.A. andMcKay, I.D.: “Laboratory Studies and Field Applicationof In-Situ Generated Acid to Remove Filter Cake in GasWells,” SPE paper 96965, presented at the SPE AnnualTechnical Conference and Exhibition, Dallas, Texas,October 9-12, 2005.11. Samuel, M., Mohsen, A.H., Ejan, A.B., Ooi, Y.S., Ashraf,S. and Nasr-El-Din, H.A.: “A Novel a-Amylase EnzymeStabilizer for Application at High Temperatures,” SPEpaper 125024, presented at the SPE Annual TechnicalConference and Exhibition, New Orleans, Louisiana,October 4-7, 2009.12. Sindhu, G.S., Sharma, P., Chakrabarti, T. and Gupta, J.K.:“Strain Improvement for the Production of a ThermostableAlpha-amylase,” Enzyme and Microbial Technology, Vol.21, No. 7, November 15, 1997, pp. 525-530.13. Machius, M., Declerck, N., Huber, R. and Wiegand, G.:“Activation of Bacillus Licheniformis Alpha-amylasethrough a Disorder Transition of the Substrate-bindingSite Mediated by a Calcium-Sodium-Calcium MetalTriad,” Structure, Vol. 6, No. 3, March 15, 1998, pp.281-292.14. Shareghi, B. and Arabi, M.: “Thermal Denaturation ofa-Amylase from Bacillus Amyloliquefaciens in the Presence20 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

of Sodium Dodecyl Sulphate,” Iranian Journal of Scienceand Technology, Vol. 32, No. A2, Spring 2008, pp. 135-140.15. Dutta, T.K., Jana, M., Pahari, P.R. and Bhattacharya, T.:“The Effect of Temperature, pH and Salt on Amylase inHeliodiaptomus Viduus (Gurney),” Turkish Journal ofZoology, Vol. 30, No. 2, 2006, pp. 187-195.16. Ajayi, A.O. and Fagade, O.E.: “Heat Activation andStability of Amylases from Bacillus Species,” AfricanJournal of Biotechnology, Vol. 6, No. 10, May 16, 2007,pp. 1181-1184.17. Reddy, N.S., Nimmagadda, A. and Rao, K.R.: “AnOverview of the Microbial Alpha-amylase Family,”African Journal of Biotechnology, Vol. 2, No. 12,December 2003, pp. 645-648.18. Tibbles, R., Parlar, M., Chang, F.F., et al.: U.S. Patent No.6,638,896, “Fluids and Techniques for Hydrocarbon WellCompletion,” October 2003.25. Barkman, J.K. and Davidson, D.H.: “Measuring WaterQuality and Predicting Well Impairment,” Journal ofPetroleum Technology, Vol. 24, No. 7, July 1972,pp. 865-873.26. Abrams, A.: “Mud Design to Minimize Rock Impairmentdue to Particle Invasion,” Journal of PetroleumTechnology, Vol. 29, No. 5, May 1977, pp. 586-592.27. Khatib, Z.I. and Vitthal, S.: “The Use of the Effective-Medium Theory and a 3D Network Model to PredictMatrix Damage in Sandstone Formations,” SPEProduction Engineering, Vol. 6, No. 2, May 1991, pp.233-239.28. Hezig, J.P., Leclerc, D.M. and Le Goff, P.: “Flow ofSuspensions through Porous Media: Application to DeepBed Filtration,” Industrial and Engineering Chemistry,Vol. 62, No. 5, May 1970, pp. 8-35.19. Luyster, M.R., Monroe, T.D. and Ali, S.A.: “FactorsAffecting the Performance of Enzyme Breakers forRemoval of Xanthan-based Filter Cakes,” SPE paper58749, presented at the SPE International Symposium onFormation Damage Control, Lafayette, Louisiana,February 23-24, 2000.20. Wood, W.D., Dennis, E.L. and Dean, G.D.: “Utilizationof Polymer Linkage Specific Enzymes to Degrade HECPolymer in Water Based Drilling and Gravel PackingFluids,” SPE paper 35594, presented at the SPE GasTechnology Symposium, Calgary, Alberta, Canada, April28 - May 1, 1996.21. Laderman, K.A., Davis, B.R., Krutzsch, H.C., et al.: “ThePurification and Characterization of an Extremely-Thermostable a-amylase from the HyperthermophilicArchaebacterium Pyrococcus furiosus,” Journal ofBiological Chemistry, Vol. 268, No. 32, November 1993,pp. 24,394-24,401.22. Raju, K.U., Nasr-El-Din, H.A., Hilab, V., Siddiqui, S. andMehta, S.: “Injection of Aquifer Water and Gas-OilSeparation Plant Disposal Water into Tight CarbonateReservoirs,” SPEJ, Vol. 10, No. 4, December 2005, pp.374-384.23. MacKay, E.J., Collins, I.R. Jordan, M.M. and Feasey, N.:“PWRI: Scale Formation Risk Assessment andManagement,” SPE paper 80385, presented at the SPEInternational Symposium on Oil Field Scale, Aberdeen,U.K., January 29-30, 2003.24. Zhang, N.S., Somerville, J.M. and Todd, A.C.: “AnExperimental Investigation of the Formation DamageCaused by Produced Oily Water Injection,” SPE paper26702, presented at the Offshore Europe Conference,Aberdeen, U.K., September 7-10, 1993.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 21

BIOGRAPHIESDr. Mohammed H. Al-Khaldi joinedSaudi Aramco in 2001 as a ResearchEngineer working in Saudi Aramco’sExploration and Petroleum EngineeringCenter — Advanced Research Center(EXPEC ARC). During this time, hewas responsible for evaluating differentstimulation treatments, conducting several research studiesand investigating several stimulation fluids. In addition,Mohammed was involved in the design of acid fracturingtreatments. As an award for his efforts, he received the VicePresident’s Recognition Award for significant contributionsto the safe and successful completion of the first 100 fracturingtreatments. Mohammed’s research interests includewell stimulation, formation damage mitigation and conformancecontrol.He received his B.S. degree in Chemical Engineering(with First Class Honors) from King Fahd University ofPetroleum and Minerals (KFUPM), Dhahran, Saudi Arabia.Mohammed also received his M.S. and Ph.D. degrees inPetroleum Engineering (with First Class Honors) fromAdelaide University, Adelaide, Australia.He is an active member of the Society of Petroleum Engineers(SPE). Mohammed has published more than 15 SPEpapers and seven journal articles, and has two patents. In2011, he received the SPE Best Technical Paper Award,winning first place in the 2 nd GCC SPE paper contest.Debayan Ghosh is the President ofEPYGEN Group, the first biotechcompany at the DuBiotech, DubaiBiotechnology Research and DevelopmentPark. Prior to starting EPYGEN,he worked for 7 years as a RegionalDirector with Dyadic Inc., located inFlorida, and 5 years with Biocon, Asia’s largest biotechgroup. Debayan has made key contributions to the developmentand research of enzyme proteins for industrial, agricultureand health care use in the region, and has authoredseveral scholastic papers in international biotech forumslike the American Chemical Society BIOT, San Francisco,World Biotech Congress, Washington, D.C.; and SPE-AsiaPacific, Jakarta, to name a few.He received his M.Tech. degree in Biotechnology fromthe Center for Biotechnology, Anna University, Chennai,India, in 1993.Dr. Bisweswar Ghosh is at present amember of the research and teachingfaculty at the Petroleum Institute (PI)Abu Dhabi, both undergraduate andgraduate levels. He has over 26 yearsof experience in the upstream oil industry,related to operations, research,consultancy and teaching. The first 10 years of Bisweswar’scareer was with the Indian multinational oil company(ONGC), managing field operations related to drilling, production,waterflood and well stimulation, and setting upvarious testing and quality assurance laboratories. The subsequent10 years were devoted to subsurface R&D, consultancyand establishment of an international standard ISOcertified production research laboratory. Prior to joining PI,he worked at Sultan Qaboos University (SQU) and PetroleumDevelopment Oman (PDO) as a full-time Research andProject Consultant. Bisweswar’s contribution to water andgas shut-off and formation damage mitigation studies hasbeen well recognized. He was associated with three of hiscountry’s premier petroleum schools as Research Advisorand Visiting Faculty.Bisweswar received his M.S. degree in PhysicalChemistry in 1984 from the Indian Institute of Technology(IIT) Kharagpur, Kharagpur, India, and his Ph.D. degree inPetroleum Chemistry (Surfactant activity of petroleumheavy fraction and the effect on tertiary recovery) in 1995from Nagpur University, Nagpur, India.He is a member of the Society of Petroleum Engineers(SPE).22 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

A Coiled Tubing Perforating Solution Incorporatinga Gun Deployment System and DynamicUnderbalance Technique Improves Well Productionin High Angle Deep Gas Wells in Saudi ArabiaAuthors: Hasan H. Al-Jubran, Jairo A. Leal Jauregui, Shaker A. Al-BuHassan, Simeon Bolarinwa, Wassim Kharrat, Dave Polson and Mazen T. BarnawiABSTRACTSaudi Aramco has recently initiated a change in gas well designin Ghawar field of Saudi Arabia. The new approach is to drilldeviated cased hole gas wells through the reservoir to increasethe length of contact with the productive zone and thereby increaseproduction potential. Typical gas wells were previouslydrilled as vertical cased hole wells through the reservoir or asopen hole horizontal wells.The increased well deviations, measured depths and resultantincrease in reservoir sections required a new approach tothe perforating solution for these wells, to connect them to thegas plants. Various techniques were reviewed, consideringsafety, operating efficiency and well performance. The final solutionwas to deploy the perforating systems on electric coiledtubing (CT) and run all the guns in one run, using completioninsertion and retrieval under pressure (CIRP) as a deploymentsystem, which allows the guns to be run and pulled under livewell conditions, without having to kill the well.This article details the learning curve and lessons learnedfrom the implementation of this technique in five gas wells.The deployment system and pressure control equipment wereoptimized to satisfy Saudi Aramco’s requirement for three barriers.A CT cleanout run was added before perforation to removeany debris from the wellbore that might cause a problemto the depth correlation tools. An existing CT tower was usedto support the very long wellhead stack, but due to its heightlimitation, a special solution was implemented to enable safeCT operations. A deployment system under live well conditionswas used to minimize CT runs and operating time, andmaximize cost savings. The static underbalance condition wasset before running the guns, combined with the dynamic underbalanceperforating technique and deep penetrating chargegun design were implemented to optimize the well performance.This technique allowed safe and efficient perforating in asingle underbalance run for these five gas wells.This article also covers planning of the perforating solution,health, safety and environment (HSE) considerations, equipmentselection, operational procedures, job execution and results.INTRODUCTIONGhawar field is a major oil and gas field in Saudi Arabia. It islocated in the Eastern part of Saudi Arabia. Measuring 280 kmlong by 30 km average width (170 by 19 miles), it is by far thelargest conventional oil field in the world. Ghawar occupies ananticline above a basement fault block dating to the Carboniferoustime, about 320 million years ago. Reservoir rocks areJurassic carbonate limestones. Regular perforating techniquesinvolve e-line interventions in vertical or S shaped wells; however,there is limited access to high angle or horizontal well paths.Over the years, coiled tubing (CT) perforating has helpedoil and gas operators to develop new reserves and maximizeunderbalance. Saudi Aramco developed a project focused ontargeting reservoir spots while benefiting from CT deployment.Implementation of this idea incorporates the use of several criticalcomponents that allowed withstanding the challengingconditions. These challenges were:• Hydrogen sulfide (H 2 S) content as high as 12%.• Deployment of long pay intervals.• CT correlation.• Stack height (100+ ft).• Proper crane selection.• Completion insertion and retrieval under pressure (CIRP)deployment system.• E-line correlation.• Single underbalanced run.• High angle well.• Use of a dynamic underbalance perforating technique.In addressing the above challenges, it was found that CTperforating provided an ideal solution, because it enabled us todo the following:• Single and optimum underbalance (for selected intervals)performance.• The possibility to combine both static and dynamic underbalanced conditions.• Avoidance of nearby gas-water contacts.• Cost savings – avoiding stimulation.• Reduced operational risk – a single run instead of multiple(pull out of hole (POOH)) runs.• Ability to retrieve long interval spent guns withoutkilling the well.• Miximize reservoir contact (kh).• Overcoming well accessibility challenges.Table 1 shows the pro and cons of CT perforating.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 23

Pros1. Improves well value(reduced initialmechanical skin).2. Maximizes wellpotential from thestart.3. Improves ßow back.4. Reduces numberof cycles, runs andoperational risk.5. Increases weightcapacity to maximizeunderbalancedcondition (critical inwell depletion cases).Cons1. Increases operationalcost.2. Requires deepunderstanding of CTlimits.3. Adds more surfaceequipment due tocomplex deployment.4. Involves complexequipment setup andprocedures.5. Requires closemonitoring of pressureto avoid CT collapse.WELL DATAFive candidates were completed as highly deviated, S-shapedgas wells requiring perforation across the carbonate Khuffreservoir (bottom-hole temperature (BHT): 300 °F to 310 °F,and bottom-hole pressure (BHP): 4,500 psi to 6,500 psi). Table2 shows the candidates’ well data and the perforating requirements.JOB DESIGNAt the design phase of the perforation jobs, different solutionsand equipment were selected to address all identified challengesto performing these jobs while avoiding any health,safety and environment (HSE) quality issues. Table 3 showsthe challenges and the proposed solutions.JOB PLANNINGTable 1. Pros and cons of CT perforatingJOB OBJECTIVEThe job objective was to safely perforate the wellbore in underbalancedconditions to maximize productivity, taking into accountthe following operational considerations:• Equipment selection.• Simulation scenarios: Fluid change and perforating operations.• CT size: 1¾” vs. 2” and 2 3 ⁄8”.• Adequate CT size (fatigue, loads).• Contingencies for CT/e-line connector.• CT POOH simulation (dry vs. loaded).• Misfire contingencies.• Surface contingencies and detailed hazard review.• Crane selection, riser length and tool weight.• Well control barriers.•H 2 S handling while breaking bottom-hole assembly(BHA) and post perforating (Cascade System).Several contingencies were added to the program to preventany unexpected event as part of Saudi Aramco’s stringent threebarriers policy. Also added were a detailed checklist, use ofHydra-Conn and a detailed hazard assessment for all operations.The perforating jobs were initially planned as follows:CT RUN 1: Well displacement• Displace the wellbore with diesel for Khuff C and 4% KClbrine for Khuff B (underbalance requirement).• Unload the required upper wellbore section with nitrogenfor Khuff C to get the desired 1,500 psi static underbalance.CT RUN 2: Drift and dummy run (e-line CT string)• Drift the well and confirm the reach of the dummy BHAto the required depth.• Prove well accessibility (add a dedicated CT run ifnecessary).CT RUN 3: Perforating run (e-line CT string)• Deployment.• Realtime depth correlation with CCL.• Perforation.• Reverse deployment.CONTINGENCY CT RUN: Sand slurry slot cutting and acidwash.JOB PREPARATIONA tool yard test was conducted, Fig. 1. In addition, a dedicatedHAZOP study was conducted of all rig-up equipment. Thepressure control equipment between the X-mas tree and the in-Well A B C D1 D2Max Deviation 53.5¡75.85¡73.4¡75.7¡75.7¡CompletionS-shapeTubing: 4½ÓS-shapeTubing: 4½ÓS-shapeTubing: 5½Ó/4½ÓS-shapeTubing: 4½ÓS-shapeTubing: 4½ÓLiner: 4½ÓLiner: 4½ÓLiner: 4½ÓLiner: 4½ÓLiner: 4½ÓReservoir Carbonate Carbonate Carbonate Carbonate CarbonatePerforation230 ft - 180 ft 260 ft - 260 ft 440 ft - 290 ft 250 ft - 120 ft 300 ft - 188 ft(Gross-Net)Table 2. The candidate’s well data24 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

ChallengesHigh deviation well with an S-shaped wellbore.Proposed SolutionsUse CT to deploy the guns to the required depth.Real-time depth correlation. • Injection of e-line cable into the CT string.• CT logging reel with surface bulkhead and collector.• E-line unit.• Running tool with CT logging head and casing collarlocator (CCL).Long gross perforation interval (ranging from 230 ft to 440ft) and underbalanced perforation (2,500 psi dynamic and1,500 psi static).• Proper selection of CT logging head and tensionlimitations.• Deployment system (CIRP) that enables insertionand retrieval of guns under pressure to perforateunderbalanced in a single CT run.Saudi Aramco requires three barriers during thedeployment of the BHA into and out of the well.• Deployment tool and deployment connectors betweenguns.• Checklists for deployment and reverse deployment.• Personnel experienced with the deployment system.• Yard test for the deployment system.• Pumping and nitrogen unit for well displacement to getrequired underbalance.Use of three gate valves as barriers in the pressure controlequipment.Multiple deployment/reverse deployment. • Dedicated yard test to confirm CIRP operation.• Use of Hydra-Conn to ease assembly and disassembly ofpressure control equipment during deployment/reversedeployment.Pressure test of the Hydra-Conn.With three gate valves below the Hydra-Conn, additionof another one on top of the Hydra-Conn to enable itspressure testing without testing the full pressure controlequipment.Sour gas in riser during reverse deployment. • Personnel training on H 2S.Deployment by two guns, 20 ft each to minimize theuse of deployment connectors (4 ft long compared to a1 ft standard connector) between the guns, thereforemaximizing the perforation extended in front of thereservoir.• H 2S monitor and cascade system at X-mas tree.• Purging of sour gas to the pit with an H 2S scavengerbefore each reverse deployment.• Connection of a high-pressure hose below the stripperand the assembly of a flow cross between the Hydra-Conn and the three gate valves to enable the purge ofthe pressure control equipment, which will be exposedduring the reverse deployment.• Testing equipment to handle purging.Use of a 60 ft riser on top of the Hydra-Conn toaccommodate the two gun’s section and the deployment ofthe BHA.Long BHA and therefore high rig up. • Use CT tower to ensure safe operation.• Yard test for the rig up of the pressure controlequipment with the CT tower.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 25

ChallengesProposed SolutionsHandling of long BHA. • CT modeling and simulation.The pressure control equipment with 60 ft of riser is higherthan the maximum height of the available CT tower.• Addition of a gun rack system to the CT tower tofacilitate the handling of long guns.• Use of a 200 ton crane for deployment/reversedeployment:Contingency: Malfunction of deployment system with nopossible movement of CT pipe.Contingency: Emergency BOP cut.• Maximum boom length = 50.6 m.• Maximum swing radius = 10 m.• Minimum safety load = 30.8 ton.• Maximum lifted load = 15 ton.• Minimum safety factor = 2.Contingency: Not able to latch the deployment connector.First implementation in Saudi Arabia of this type of job. • Detailed job program.Table 3. The CT perforating challenges and proposed solutions• Shortening of the 60 ft risers used during deploymentand reverse deployment to 18 ft during RIH/POOH toenable setting the injector head on top of the CT towerto get a fully stable rig up independent of the crane.Addition of a 10 ft riser below the three gate valves toaccommodate the remaining BHA after it is disconnectedfrom the CT logging head. The three gate valves could bethen closed.Placement of a combine BOP just below the stripper toguarantee emergency cut of CT pipe (not guns) whenneeded.Use of a fishing tool.• Yard test for all new implemented equipment.• Experienced people for deployment of BOP (fromabroad).• Designated team to build experience with this type ofjob.• Checklists detailing step-by-step procedure of all criticaltasks.• Contingency plan.• Detailed hazard assessment and risk control analysis.jector head is described in Table 4. All the pressure controlequipment was 10 K psi rated; the deployment blowout preventer(BOP) and the Hydra-Conn (CIRP) were 15 K psi rated.CIRPCIRP is a technique for deploying long gun strings under pressurewhen the lubricator is shorter than the gun string. It isparticularly useful for retrieving long gun strings from a perforatedwell without killing the well. The three main componentsof the system are: (1) The CIRP connectors (to connect the gunsections together), (2) The CIRP deployment stack (to operatethe well under pressure), and (3) Gate valves (barriers to isolatethe lubricator from the well pressure).CIRP Connector. The connector, Fig. 2, is the mechanical andballistic link between the gun sections in the lubricator. TheCIRP lower section has a spring-loaded lock sleeve, which hasto be rotated clockwise to unlock the connector and disconnectthe upper section. Both sections are locked together when thesleeve is rotated back and held in the locked position by thetorque spring. Both sections have a sealed ballistic transfer;donor transfer on the top (trigger charge), and the receiver onthe bottom (receptor booster). All explosives used for theballistic transfer are secondary explosives. A slick joint at thebottom of the connector has a landing shoulder for precisepositioning in the deployment stack.26 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Fig. 2. Standard CIRP connector.Fig. 1. Tool yard test.DescriptionTwo CT BOPs with crossoverto the X-mas tree.Deployment BOP (CIRP)with cross-overs.FunctionSecondary and tertiarybarriers.To deploy/reverse deploy along BHA under pressure.10 ft riser. To accommodate theremaining BHA afterdisconnecting it from the CTlogging head to close thethree gate valves.Two hydraulic activatedgate valves and a manualone.Flow cross with fourhydraulic activated gatevalves.Hydra-Conn with crossovers.Manual gate valve.Three barriers duringdeployment/reversedeployment.Pressure testing, bullheadingand purging.To facilitate the assembly/disassembly of pressurecontrol equipment formultiple deployments/reverse deployments.To limit the pressure testbetween the two gatevalves every time the Hydra-Conn is operated.60/18 ft riser. 60 ft during deployment/reverse deployment and 18ft during RIH/POOH.Combine BOP with twohydraulic activated valvesand a high-pressure hose.Over/under stripper withanti-buckling guide.Table 4. The pressure control equipment used in the jobsContingency for the stripperleak and emergency BOPcut. Purging of sour gasduring reverse deployment.Primary barrier.Fig. 3. Standard CIRP deployment stack.CIRP Deployment Stack. The CIRP deployment stack, Fig. 3,is installed at the bottom of the lubricator below the gatevalves. It can be operated hydraulically or manually. It includestwo sets of special CIRP ram actuators mounted on aCT combo BOP body that activate the lower no-go rams withlock inserts and the upper guide rams with rack inserts.The lower set of actuators closes the no-go ram around theslick joint to provide a shoulder to hang the string on. Theconnector is then landed in the ram and the lock inserts areactivated to grip the connector. This locates precisely the locksleeve actuator (rack) and locks the connector against rotation.The second set of actuators closes the guide ram around a“pinion” profile on the lock sleeve. Then the rack’s inserts engagethe lock sleeve and rotate it against the torque spring.The spring can then be pulled up to disengage the upper sectionof the CIRP connector.Gate Valves. After actuating the CIRP stack to disconnect theCIRP connector, the upper gun section is pulled up in the lubricatorabove the gate valves. The gate valves are then closedand the lubricator is bled off for retrieving the guns. The gatevalves are effectively sealing the well while the gun is hangingin the CIRP stack below the valves and keeping the well underpressure while the gun section is retrieved. Note that the pressurebarrier is the gate valves and not a pipe ram type seal onthe connector. Table 5 shows the CIRP deployment systemspecifications.The high-pressure/high temperature (HP/HT) 15 K CIRP,SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 27

Gun Size 2” 2½” 2 ” 3 ” 3½” 4½”ConnectorsOutside Diameter, mm (in) 57.2 (2.25) 57.2 (2.25) 71.171.1 (2.8) 71.1114.3(2.8)Temperature Rating*, °C (°F) 204 (400) 204 (400) 204(2.8)204 (400) 204(4.5)204(400)(400)(400)Collapse Pressure † , MPa (psi) 138138138138138138(20 K)(20 K)(20 K)(20 K)(20 K)(20 K)Shot-to-shot Distance ‡ , cm (in) 117117117117117119(46)(46)(46)(46)(46)Makeup Length, cm (in) 86.2 (33.94) 86.2 (33.94) 85.6 (33.7) 85.6 (33.7) 85.6(47)89.7(33.7) (35.33)Slick Joint Length, cm (in) 30.5 (12) 30.5 (12) 30.1 (11.84) 30.1 (11.84) 30.1 (11.84) 30.5(12)Tensile Strength † , kN (lbf) 2672676456456451,668(60 K)(60 K)(145 K)(145 K)(145 K)(375 K)Compressive Strength † , kN (lbf) 85852272272271,312Nominal Rotation of LockSleeve, °Rack and LockCIRP Combine BOP Stack ID,mm (in)Working Pressure, MPa (psi) 69(19 K) (19 K) (51 K) (51 K) (51 K) (295 K)15 15 15 15 15 15103 (4.06) 103 (4.06) 10369(4.06)69103 (4.06) 10369(4.06)69130(5.125)69Ram Space Out, Center toCenter, cm (in)Max. Downward Load on NoGo Rams, kN (lbf)Max. Upward Pull on No GoRams, kN (lbf)(10 K) (10 K) (10 K) (10 K) (10 K)29.2 (11.5) 29.2 (11.5) 29.2 (11.5) 29.2 (11.5) 29.289(20 K)89(20 K)89(20 K)89(20 K)178(40 K)178(40 K)*For 100 hours, the temperature rating can be increased with special seals.†Collapse pressure rating is at 67% of yield strength; tensile and compressive strengths are at yield strength.‡Nominal shot-to-shot distance, exact distance depends on shot density and phasing option of gun.178(40 K)178(40 K)(11.5)178(40 K)178(40 K)(10 K)36.8(14.5)378(85 K)378(85 K)Table 5. CIRP deployment system specificationsFig. 4, is a deployment system that has been modified to seal offwell pressure from the lubricator riser during reverse deploymentafter the guns are fired. The 15 K CIRP deployment stack isequipped with a third set of rams at the bottom (pipe rams) toisolate the upper part of the stack from the wellbore pressure. Thestandard CIRP connectors have been enhanced to include a sealingsurface for the pipe rams to close against, and an inside checkvalve (flapper) has been added to contain the inside pressure oncethe guns have been shot. Details of the run in hole (RIH) and pullout of hole (POOH) rig up and deployment/reverse the deploymentrig-up are shown in Figs. 5 and 6, respectively.CT TowerA 55 ft CT tower, with flexible height, was available for the CTperforating jobs, Fig. 7. The crane will still be needed to deployand reverse deploy the guns, but during RIH/POOH the injectorhead is supported by the fifth floor with additional legs.The advantages of the CT tower are:28 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Fig. 4. HP/HT 15 K CIRP deployment stack.Fig. 5. RIH/POOH rig up.Fig. 7. CT tower rig-up during deployment/reverse deployment.• Use of a gun tack system to increase efficiency of gunmakeup and lay down.Bottom-hole AssemblyFig. 6. Deployment/reverse deployment rig up.• The entire pressure control equipment stack is chained tothe tower, which eliminates any stress on the connections.• Minimize nonproductive time caused by crane andweather during RIH/POOH.• Easy access to the injector head and all pressure controlequipment at any time without the use of an additionalcrane and man basket, which facilitates the operation ofthe Hydra-Conn.Different BHAs were needed for the perforation run as follows:• Deployment tool: Needed to deploy the downholegauges initially with one blank/loaded gun, then two gunsfor every deployment. Reverse deployment was also donewith this tool.• Running tool: Needed to run the full perforating BHA.• Fishing tool: Needed as a contingency in case the reversedeployment could not be done with the deployment tool.• Gamma ray (GR): Added to the BHA after the first job.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 29

Fig. 9. Example of underbalance perforating simulation, Well-A.Fig. 8. Example of a loaded 2⅞" gun string, Well-A.Perforating Guns Design and SelectionBased on the reservoir log, the loading of the guns and theplacement of the 4 ft deployment connectors and 1 ft standardadapters, were optimized to maximize perforation across thebest quality reservoir rock. A simulator was used to select theperforating gun system and the optimal hydrostatic pressure atthe time of perforation. The 2 7 ⁄8” HSD gun system loaded withdeep penetrating charges was used to maximize formation penetrationand reservoir contact area. Shot density varied from 3SPF to 6 SPF. An e-line run firing head was used as a detonatingsystem and a gauge was used to capture the dynamic underbalance.A new design was used in some wells to cleanupthe perforating tunnels and enhance well performance. Figure8 shows an example of a loaded 2 7 ⁄8" gun string used in Well-A. Figures 9 and 10 show examples of simulation and fastgauge data, respectively, for Well-A.EXECUTION AND LESSONS LEARNEDAfter competing the job preparation, the perforation of Well-Awas performed as the first worldwide land job with the 15 Kpsi rated deployment BOP (CIRP).Some operational issues were encountered and additionaljob requirements were put in place based on the lessonslearned, Table 6.Fig. 10. Example of fast gauge actual pressure data recorded in Well-A at dynamicunderbalance conditions.The addition of the cleanout, with a high-pressure jettingtool and high temperature gel, was very beneficial, as cement,rubber and/or metallic pieces were recovered at the surface inevery well.All of the above action plans were implemented for the secondjob in Well-B, during which some additional problemswere noticed. The root causes were identified, and the problemswere resolved once and for all, Table 7.Starting with Well-C, the nonproductive time, which canstill be eliminated, was caused by the crane’s working limitationrelative to the wind speed. A higher CT tower is requiredto minimize this type of nonproductive time during deploymentand reverse deployment.Starting with Well-D, the dummy/drift run was eliminatedafter adding a drift ring to the high-pressure jetting tool, simulatingthe biggest outside diameter of the perforating BHA,which is the one for the downhole gauges (3.06”).After building experience with the three first jobs, the finalCT perforating job program was set as follows:CT RUN 1: Wellbore cleanout and well displacement.• Use a high-pressure jetting tool and high temperature gelto clean out the wellbore.• Displace the wellbore with diesel for Khuff C and 4% KCl30 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

ProblemsDifÞcult communicationbetween Saudi Aramco anddifferent service companies.Depth control problem dueto very high rig up: A smallCT pipe movement at theinjector was not capturedby the depth encoder at theCT reel.Uncontrolled activationof the CT logging headdisconnect duringdeployment (over pullrequired by the deploymentBOP) due to the depthcontrol problem citedabove.Noisy CCL correlation dueto metallic debris insidethe well, which also causedover pull close to the limitof the CT pipe.Electric leak after highpressuretest (7,500 psi) atthe surface and downholebulkhead.Action PlanAssign the CT engineer as afocal point.Install the depth encoder atthe gooseneck level.Use a ßow/tension releasedisconnect instead ofa tension/cycle releasedisconnect.¥ Add a wellbore cleanout,with HT gel and a HPjetting tool, to the welldisplacement run.¥ Use a combination GR/CCL tool instead of asingle CCL tool.¥ Use 2Ó CT string having90,000 psi yield strengthinstead of 1¾Ó CT stringhaving 80,000 psi yieldstrength.¥ Perform a full systemcheck at the yard afterperforming the test.¥ Create a troubleshootingchecklist.ProblemsElectric leak after highpressure test (7,500 psi).Long reverse deploymentdue to hydrate formation.Long reverse deploymentdue to debris into thedeployment connector.Table 7. Lessons learned from Well-B perforating jobsAction Plan¥ Improve cable injectionprocess and protectcable during pressuretest to avoid invasion ofcable with water.¥ Use different surfacebulkhead with bettersealing.¥ Inject new e-cable.Change the purgingprocess: The bleeding ofthe riser should be doneafter disconnecting fromthe BHA and isolatingthe well with the gatevalves and not afterclosing the pipe ram of thedeployment BOP.Modify the Þshing tool toenable jetting through it,which will help to clean thedeployment connector andfacilitate latching into it.Long time needed fordeployment of blank gunsto simulate full BHA lengthfor the dummy run.Long time needed todisassemble the riser afterdeployment and assemble itfor reverse deployment.Leak at the hydrauliccontrol line of one gatevalve.Table 6. Lessons learned from Well-A perforating job¥ E-line engineer to useappropriate tools, andprocedure to redresslogging equipment, andtroubleshoot it.Perform the drift runwith just 2 guns (1 singledeployment).Add a riser stand to the CTtower to remove/add the 42ft riser in one piece.Change the workingpressure of the line from3 K psi to 6 K psi ratedpressures.brine for Khuff B.• Unload the required upper wellbore section with nitrogenfor Khuff C to get the desired 1,500 psi static underbalance.CT RUN 2: Perforating run (e-line CT string).Fig. 11. Debris collected from Well-D1.• Deployment.• Real time depth correlation with GR/CCL.• Ensure loaded guns are targeting the best quality rock (notconnectors/blank guns) and eliminate any depth errorcaused by the elastic/plastic deformation of the CT pipe.• Perforation.• Reverse deployment.CONTINGENCY CT RUN: Sand slurry slot cutting and acidwash.OPERATION ANALYSISWellbore CleanoutThe high-pressure jetting tool was used successfully to removeany cement in the wellbore. The high temperature gel, adequatefor a BHT as high as 325 °F, was used successfully to liftall debris (large pieces of cement, rubber and/or metallicpieces), Fig. 11, to the surface without the need for nitrogenSAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 31

pumping. A Venturi junk basket was run in Well-D after thefirst cleanout run and confirmed the efficiency of the highpressurejetting tool and the high temperature gel. The averagerequired time for the first wellbore cleanout run is 0.3 hours/ftof gross perforation interval.Fig. 12. CT tower rig-up and rig-down time.CT Tower Rig-Up/Rig-DownTime for CT tower rig-up, Fig. 12, and pressure testing of thepressure control equipment time was reduced from 60 hours inthe first well, Well-A, to 43 hours in the last couple of jobs inWell-D (28% job time reduction). The CT tower rig-downtime was reduced from 19 hours in the first well, Well-A, to 11hours in the last job, Well-D (42% job time reduction).Dummy/Drift RunFig. 13. Dummy/drift running time.In Well-A, the full length of the blank BHA (250 ft) was deployedfor the dummy/drift run, Fig. 13. In Well-B and Well-C,only two guns (one single deployment) were deployed for thedummy/drift run. The deployment time was reduced from 28hours to 13 hours, showing experience buildup within the team(53% job time reduction). After eliminating the dummy/driftrun, the total job time was reduced by almost two days.Perforating RunFig. 14. Perforation running time.Fig. 15. Deployment and reverse deployment speed.Excessive time (61 hours) for reverse deployment was noticedin Well-B due to hydrate formation and debris in the deploymentconnector. The purging procedure and fishing tool weremodified to optimize the reverse deployment time. The targettime of three days for the perforation run was almost reachedin the last couple of jobs, Fig. 14.As the gross perforation length changed from one well toanother, a normalized deployment/reverse deployment speedwas calculated for all wells in feet of gross perforated intervalper hour. The deployment speed increased from 3 ft/hour inWell-A to 9.4 ft/hour in Well-D’s last job. The reverse deploymentspeed increased from 7.2 ft/hour in Well-A to 11.1ft/hour in Well-D’s last job. Figure 15 provides details on thedeployment/reverse deployment speeds from the last job.NONPRODUCTIVE TIMEFig. 16. CT and testing nonproductive time per well, Well-A and Well-B.Fig. 17. Normalized time – CT perforating analysis per well (hrs/ft).There were 97 hours of nonproductive time recorded on theCT during the first implemented job in Well-A due to depthcorrelation during deployment (23 hours), and an electric leakat the surface bulkhead (44 hours) and at the downhole bulkhead(30 hours). There were 64 hours of nonproductive timerecorded on the CT during the second job in Well-B due to anelectric leak at the e-line cable, Fig. 16.After troubleshooting, a new logging reel was prepared,pressure and function tested, then mobilized to location. TheBHA was then assembled and pressure and function tested.The last three perforating jobs were done with zero nonproductivetime on the CT.32 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

WellGrossPerf (ft)CTTowerRig-Up(hrs)Dummy/Drift(hrs)Perforation(hrs)CTTowerRig-Down(hrs)CTNPT(hrs)Total Job Timew/oCT NPT(days)withCT NPT(days)DeploySpeed(ft/hr)RevDeploySpeed(ft/hr)Total Job Time(hrs/ft Gross Perf)A 230 60 119 76 19 97 11.4 15.5 2.9 7.2 1.68 2.12B 260 66 61 100 18 64 10.2 12.9 3.2 5.9 1.53 1.78C 440 52 55 87 24 0 9.1 9.1 10 8.5 0.59 0.59D1 250 43 0 76 23 0 5.9 5.9 7.8 9.6 0.53 0.53D2 300 43 0 75 11 0 5.4 5.4 9.4 11.1 0.43 0.43Table 8. Perforation timing of all the wellsw/oNPTwithNPTWellChokeWHPGas RateNote(/64)(psi) (MMscfd)A 64 2,065 32.5 New well (expectation 25 MMscfd at 1,400 psi).No matrix stimulation after perforation.B 38 1,160 6.3 Sidetracked well, excessive LCM material lost.64 1,800 27.9 After five slot cuttings and acid/solvent wash.64 2,455 37.7 After matrix acid.C 64 2,175 33.9 No matrix stimulation after perforation.Table 9. Wells production data after perforatingJOB TIMING SUMMARYTable 8 summarizes the perforation timing in each well.The total perforation time was reduced from 15.5 days inWell-A to 5.4 days in Well-D (65% reduction in total job time)even though the gross perforation interval increased from 230ft to 300 ft. From Well-A to Well-D, the normalized total perforationtime decreased from 2.12 hours/ft to 0.43 hours/ft ofgross perforated interval (an almost 80% reduction in totalnormalized job time), Fig. 17.RESULTS1. The five perforation jobs were done with continuous improvementin service quality and reduction in total job timing.2. Zero CT nonproductive time was achieved starting from thethird job.3. 100% efficiency of the deployment BOP (no malfunction).4. 100% efficiency of the firing head (no miss fire).5. The shot density at the good reservoir zone was maximizedwith proper placement of deployment connectors and byreal time depth correlation.6. Reverse deployment was conducted safely at a wellheadpressure up to 4,000 psi and 12% H 2 S content.7. Contingency reverse deployment was implemented successfully with a modified fishing tool.Production from the wells after perforating is summarizedin Table 9, which shows how successful this newly implementedtechnique was in Saudi Arabia.CONCLUSIONS AND RECOMMENDATIONSThis CT perforating technique, relatively new to Saudi Arabia,was safely and successfully implemented. All the challengesthat almost resulted in the failure of the technique were overcomewith the due diligence and dedication of the whole teaminvolved. This perforating technique has been proven to deliverresults and can be employed in Saudi Arabia when required,now and in the future. In summary:1. CT perforating proved beneficial for deploying live gunsunder a high-pressure scenario as well as in cases of limitedaccess.2. Single run perforating underbalanced proved valuable, whileavoiding the need for stimulation in three out of seven jobscompleted.3. The use of CT e-line technology provided good correlationwhile perforating.4. The system proved 100% reliable while having no missedruns.5. There was no operational incident, and the CIRP modularperforating system proved its great value while not leavingany guns.6. The objectives were met, and this new technology has beenrecommended for extended application.7. This new technology proved valuable, not only because ofsavings compared with regular rig perforating costs, butalso because of increased well potential and stimulationavoidance.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 33

8. CT perforating is competitive with other perforating techniques(like CT hydrajetting) providing another alternativewith its single run/underbalance approach as well as costavoidance.ACKNOWLEDGMENTSThe authors wish to thank Saudi Aramco management and theGas Production Engineering Division, as well as Schlumbergerin Aberdeen, Scotland, and Saudi Arabia, for the permission topublish this article. Thanks also go to all the people involvedin making the implementation a success.BIOGRAPHIESHasan H. Al-Jubran is a ProductionSpecialist with the Gas ProductionEngineering Division of the SouthernArea Production Engineering Department(SAPED). He has 17 years of oilproduction experience.In 1992, Hasan received his B.S.degree in Petroleum Engineering from King Fahd Universityof Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia.He is a member of the Society of Petroleum Engineers(SPE).Jairo A. Leal Jauregui is a Senior PetroleumEngineer in the Gas ProductionEngineering Division of the SouthernArea Production Engineering Department(SAPED). He has 19 years ofexperience in the oil and gas industryin areas like workovers, acid stimulation,and perforating and fracturing, and with operations inColombia, Venezuela, Argentina and Saudi Arabia. Jairohas authored several Society of Petroleum Engineers (SPE)papers on field technology applications, fluids andstimulation results.In 1990, Jairo received his B.S. degree in PetroleumEngineering from the Universidad Industrial de Santander,Bucaramanga, Colombia, and a Specialization in ProjectManagement from Pontificia Universidad Javeriana,Bogota, Colombia, in 2005.Shaker A. Al-BuHassan is a SeniorProduction Engineer with the ‘UdhailiyahProduction Engineering Divisionof the Southern Area ProductionEngineering Department (SAPED). Hehas over 16 years of oil productionexperience.In 1992, Shaker received his B.S. degree in PetroleumEngineering from King Fahd University of Petroleum andMinerals (KFUPM), Dhahran, Saudi Arabia.He is also a member of the Society of PetroleumEngineers (SPE).Simeon Bolarinwa is a Senior PetroleumEngineer with Saudi Aramco’sGas Production Engineering Divisionof the Southern Area ProductionEngineering Department (SAPED).Prior to joining Saudi Aramco in 2009,he worked for Schlumberger WellServices, Coiled Tubing Division, for 12 years in variouscapacities in West Africa and North America.In 1995, Simeon received his M.S. degree in MechanicalEngineering from the (now-named) Gubkin Russian StateUniversity of Oil and Gas, Moscow, Russia, specializing indrilling machines and equipment.34 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Wassim Kharrat has been working forSchlumberger since 1998. He built histechnical and operational expertise incoiled tubing and matrix stimulation.Wassim started his career in Tunisia asa Field Engineer, and then worked asan engineer in charge in Germany andLibya, before being re-assigned to the USA as a coiledtubing worldwide technical support engineer (InTouch).Currently, he is working as the Coiled Tubing Technicaland Sales Manager for Saudi Arabia and Bahrain, with themain focus on the introduction and implementation ofFiber Optic Enabled Coiled Tubing new technology.In 1998, Wassim received his M.S. degree in Mechanicaland Industrial Engineering from École Nationale Supérieured'Arts et Métiers (ENSAM), Paris, France.Dave Polson is now based in Schlumberger’sLondon office as the GlobalAccount Manager for Testing Services,supporting U.K. based clients withtheir international operations. Prior tohis current assignment, he was thecompany’s Perforating DomainChampion, based in Saudi Arabia for a period of 3 years.Dave originally joined Dowell Schlumberger in 1980 beforemoving to Flopetrol Schlumberger, spending a numberof years in the North Sea/U.K. operations, including workingas U.K. Operations Manager for the downhole groupcovering perforating, DST and completions tools. He hasalso held various operational management positions aroundthe world, including in Indonesia and the Far East.Dave spent an extensive period in the Middle East, basedout of Abu Dhadi, as Operations Manager, covering testing,completions and artificial lift activities for the Gulf countries.In 2003 he moved to Texas to become the Global BusinessDevelopment Manager for Perforating Services basedout of the Rosharon Perforating and Research Center inRosharon, Texas.Mazen T. Barnawi is currently the PerforatingDomain Champion at SchlumbergerMiddle East Saudi Arabia, andhas been with the company for 12years. At Schlumberger, he started fieldengineer conducting surface and downholewell tests and TCP perforatingoperations, mainly in the Gulf of Mexico. Mazen laterserved as Field Services Manager, then as MarketingManager for the company’s well testing division in Kuwait.In between, he served on various technical posts in thecompany.In 1999, he received his B.S. degree with honors inPetroleum Engineering from King Fahd University of Petroleumand Minerals (KFUPM), Dhahran, Saudi Arabia.Mazen went on to receive his M.S. degree in PetroleumEngineering in Enhanced Oil Recovery from Texas A&MUniversity, College Station, Texas, in 2008.He has been a member of the Society of PetroleumEngineers (SPE) since 1995, and has served twice aspresident of the KFUPM SPE student’s chapter.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 35

First Borehole to Surface ElectromagneticSurvey in KSA: Reservoir Mapping andMonitoring at a New ScaleAuthors: Dr. Alberto F. Marsala, Muhammad H. Al-Buali, Zaki A. Ali, Dr. Shouxiang M. Ma, Dr. Zhanxiang He, Tang Biyan,Guo Zhao and Tiezhi HeABSTRACTThe first Borehole to Surface Electromagnetic (BSEM) pilotfield survey in Saudi Arabia was successfully executed to identifyoil and water bearing reservoir layers of a carbonate oilfield water injection zone 1 .Maximizing the recovery factor through detailed mappingof hydrocarbon accumulations in a reservoir is a key requirementfor oil producing companies. This mapping is done routinelyby making accurate measurements of fluid distributionat the wells’ locations, but a knowledge gap exists in the interwellvolumes, where typically only density-based measurements(seismic and gravity) are available. Density technologiesare not always effective in discriminating and quantifying thefluids in the porous space (especially when the difference influid densities is small, such as in oil and water). On the otherhand, when high electrical resistivity contrasts exist betweenhydrocarbons and water, electromagnetic (EM) based technologieshave the potential to map the distribution of the fluidsand monitor their movement during the life of the field, hundredsof meters or kilometers (km) away from the wellbores.The objective of a BSEM survey is to obtain fluid sensitiveresistivity and induced polarization (IP) maps. These are basedon an acquisition grid at the surface, a few km around the EMtransmitting well, and they reveal oil and water bearing zonesin the investigated reservoir layers.In this pilot field test, the BSEM technology showed the potentialto map waterfront movements in an area 4 km from thesingle well surveyed, evaluate the in sweep efficiency, identifybypassed/lagged oil zones and eventually monitor the fluid displacementsif surveys are repeated over time. The data qualityof the recorded signals is highly satisfactory. Fluid distributionmaps obtained with BSEM surveys are coherent with productiondata measured at the wells’ locations, filling the knowledgegap of the inter-well area.Three key R&D objectives for this BSEM pilot were achieved.First, the capability to record at the surface, the EM signals generatedin the reservoir. Second, the pilot proved the capability ofBSEM surveys to discriminate between oil and water saturatedreservoir zones. Third, resistivity maps and a fluid distributionestimate were obtained that were plausible and coherent withthe information obtained from well logs, crosswell EM, productiondata and reservoir models.In addition to reservoir monitoring, BSEM surveys can bevery useful in nondiagnosed areas like exploration fields forhydrocarbon exploitation.INTRODUCTIONMaximizing the recovery factor — by identifying the locationsof hydrocarbon accumulations in a reservoir — is a key requirementfor oil companies. It is possible to get an accuratemeasurement of fluid distribution (hydrocarbon saturation) atthe wells’ location near the wellbores, thanks to productiondata, logs, core measurements, etc. 2 A knowledge gap exists inthe inter-well volumes, where typically only density contrastbased measurements (seismic and gravity) are available. Densitytechnologies are very powerful for outlining the rock structuresand characterizing the reservoir, but they are not similarlyefficient in discriminating between the fluids in theporous space (typically oil and water). When high electrical resistivitycontrasts exist between hydrocarbons and water, electromagnetic(EM) based technologies have the potential tomap the distribution of the fluids and monitor their movementduring the life of the field, even hundreds of meters or kmaway from the wellbores.BSEM TECHNOLOGYThe Borehole to Surface Electromagnetic (BSEM) method, intime and frequency domain, is the evolution of controlledsource EM technology, a surface to surface EM technique.Fig. 1. BSEM layout.36 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Fig. 2. Surveyed area with receivers’ lines, well locations and water injection front,relative to the BSEM acquisition grid.Fig. 4. Porosity log of the surveyed well showing fluid saturation and BSEMtransmitter locations (A1, A2, A3 and A4) across the selected reservoir layer.Fig. 3. BSEM transmitting electrode A, ready to be deployed into the wellbore.The BSEM method was first employed in Russia at thebeginning of the new millennium and has been extensivelyimproved in recent years in China, obtaining positive results 3, 4 .In the BSEM method, the transmitting electrode is located ina wellbore, and the receiver array constituting hundreds ofelectrodes, is placed at the surface, buried a few feet under theground, Figs. 1 and 2.The transmitting dipole consists of two electrode points: Asurface grounding point, the electrode B is buried a few metersfrom the wellhead, and electrode A, Fig. 3, is located in thewellbore.Electrode A, deployed through a classic wireline operation,can be placed at different depths, though it is typically locatedat the top and the bottom of the reservoir layer under investigation,Fig. 4.The AB dipole source transmits a variable frequency, squarewave and current signal. At the surface, the receiver arraymeasures amplitude and the phase of the electric field’s radial(Er) component, oriented from each receiver station toward thesurveyed transmitter well.Laboratory experiments 5 on cores at various oil saturationshave shown that the resistivity of oil saturated samples is frequencydependent (in a very low frequency range). On theother hand, water saturated core samples are characterized byconstant electrical resistivity in the same frequency range. Thisphenomenon is known as induced polarization (IP). The BSEMSAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 37

method is characterized by the observation that the solid-liquidinterface of rock/oil/water can produce IP effects and frequencyscattering responses (FSR) triggered by an EM fieldtransmitted in the reservoir through multiple low frequencysweeps. These IP and FSR phenomena are supported by extensivelaboratory experiments on cores 6 . Specifically, the phenomenaof IP and FSR are evident at the water-oil interface wherephysical chemistry reactions have been activated by external EMfields.Analysis of resistivity and IP anomalies in the field forms thebasis of the BSEM capability to map the oil and water presencein a surveyed reservoir layer.PILOT BSEM SURVEYThe first BSEM pilot field survey in Saudi Arabia was executedin an observation well within a carbonate reservoir, as part ofa collaborative R&D project between Berri Gas Plant (BGP)and Saudi Aramco. In this oil field, reservoir pressure is maintainedthrough a peripheral water injection program, where injectedwater replaces produced oil. The selected area is a transitionzone between water injector wells and oil producerwells, where water distribution could be influenced by reservoirheterogeneities. Because of the high resistivity contrast betweenoil and injected water, it is possible to associate thezones of variable fluid saturation with formation resistivityvariations. These would likely be visible by applying boreholebased EM techniques, such as the BSEM method.Recently, Schlumberger and Saudi Aramco successfully concludeda joint R&D project to investigate the potential ofcrosswell EM technology to map the oil and water filled zonesin a reservoir volume encompassed by three wells in the samearea of the field 7 .The rationale of choosing this area for the BSEM study wasthe availability of cores, logs and production data from the numerouswells nearby, as well as fluid distribution maps of theinter-well volumes from the crosswell EM survey. While allthese data were not used during the BSEM processing and interpretation,they allowed the subsequent validation of theBSEM survey outcomes.In this BSEM field pilot, EM signals were transmitted fromfour different open hole locations (A1, A2, A3 and A4) acrossthe selected reservoir layer, Fig. 4, and Er was recorded at 817receiver stations on the surface. The receiver grid, Fig. 2, rectangularlycentered on the transmitter well, was composed of fourlines 4.6 km long and eight lines 2.3 km long. An additional receiverline was deployed to assess the maximum distance fromthe transmitter wellhead where the signal could be received.From an R&D standpoint the pilot field test had three key expectedoutcomes:• Demonstrate the capability to record, at the surface an EMsignal generated in the reservoir, thereby overcoming thepotential signal shielding of the anhydrite layers in the overburdenand in the complex karst desert environment in thenear surface layers.• Demonstrate the capability to discriminate between oil andwater saturated reservoir zones.• Obtain from the BSEM survey a fluid distribution estimatethat is plausible and coherent with information obtainedfrom well logs, production data, reservoir models andcrosswell EM surveys.FIELD OPERATIONSIn the pilot study, BSEM field operations were conducted forthe first time in Saudi Aramco. Extensive job planning wasconducted, including the development of hazard assessmentsand “what-if” scenarios. Consequently, several proactive actionswere taken, ensuring effective measurements and safe operations.The surveyed well was completed with 4 1 ⁄2” tubingwith a minimum restriction of 3.725”. The transmitter electrodewas 47 ft in length with a 2.76” outer diameter. A 3 1 ⁄2”gauge cutter was run beforehand to ensure accessibility to totaldepth (TD). One of the main challenges in BSEM operations isthat the wireline cable has a larger cross section (0.47” diameter)compared to conventional wireline operations, to withstandthe high voltage and current required during the survey.Special wellhead control equipment was used, including ablowout preventer (BOP) capable of holding or shearing thisexceptional wireline in case of emergency. In addition, killfluid was provided on-site and hydrogen sulfide (H 2 S) detectorsand alarms were placed around the area. The field job wasperformed safely and successfully in seven consecutive days.SURVEY RESULTSSignal Quality and De-noise ProcessingTwo sets of 24 channel receivers were used to record all 817stations in a rolling spread manner. Once data acquisition ofone spread is concluded, a fast pre-processing allows dataquality control, if it is unsatisfactory, data acquisition is repeatedimmediately. In the surveyed area, there were a seriesof pipelines, power lines, well casings and metal facilities,which can produce a relatively strong EM noise. The effect ofthese structures on the recorded noise was simulated and removedduring data pre-processing; moreover backgroundnoise before and after BSEM transmission, was recorded ateach station, and frequent repeatability tests were performedduring the survey.One of the key challenges in BSEM is how to reduce andcope with the external EM noise, improving the signal to noiseratio (SNR). The several sources of EM interferences in thesurveyed area included:• EM interferences caused by industrial power supply and natural magnetotelluric (MT) signals.• Secondary EM signals caused by the presence of aboveground and buried pipelines and the well casing.• Cultural noise induced by activities, such as traffic, vehiclesmoving, fluid pumping, etc.38 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Fig. 5. Fourier spectral analysis of MT signals.Fig. 6. De-noising procedure applied to measured data at line number 10,transmitting in the wellbore at location A1.Figure 5 shows the Fourier spectral analysis of natural MTsignals recorded before the survey. Three kinds of interferenceswere observed, characterized by three different frequencies: themain interference was at 50 Hz to 60 Hz and their harmonics,originated by power lines. Its maximum value reached 120.4µmV/m at peak, but its average value was around 5.5 µV/m;and its interference level was relatively low in the surveyedarea. A second kind of interference was observed at 60 Hz. Athird kind of interference occurred at 120 Hz to 180 Hz.Several tools are available for reducing the effects of theseinterferences on the measured signals:• Monitor natural MT field variations and record naturalbackground signals for 5 minutes each before and aftertransmitting BSEM signals. This data average is used as a“blank” to be subtracted from the BSEM raw measureddata.• Create a 3D model of aboveground and undergroundpipelines and casings, simulate their secondary responsesafter EM transmission, then subtract these effects from themeasured BSEM signals.• Reduce the random interferences caused by vehicular traffic,pumps, excavators, etc., then perform a numericalsmoothing and filtering of the signal curves.Figure 6 shows the process of reducing the effects of noiseinterference on the acquired raw signals. Figure 6a displays acurve of raw signals recorded at all the station points of a selectedreceiver line (line 10) during BSEM transmission at afrequency of 0.5 Hz. Line 10 is 4.6 km in length and it has 92station points, spaced at 50 m each and labeled with numbers150 to 243 in the graphs’ abscissas. Figure 6b is the curve ofthe background noise recorded before and after the BSEMtransmission. Figure 6c displays the simulated effects on eachspecific receiver station, at the same transmission frequency, ofall the pipelines and casing in the surveyed area, obtainedthrough 3D modeling of the conductor structures. Figure 6d isthe curve resulting after subtracting the effects of Figs. 6b and6c from the raw signals of Fig. 6a. It is evident how, in relativeterms, the signal amplitudes of Figs. 6b and 6c are well belowthe raw signal amplitudes plotted in Fig. 6a. The data on allthe recorded lines in the survey is generally accurate, with aSNR of about 15. Figure 6e displays the final data after filteringand smoothing. The receiver stations at the center of line10 (around receiver station 191) clearly recorded higher amplitudesignals relative to those at the extremities of the line(around receiver station numbers 160 and 240). This is due tothe fact that the transmitter well was located near the center ofline 10. All the receivers’ data were consequently normalizedfor their distance from the transmission wellbore.All the measured raw signals were pre-processed in this waybefore the data processing in the time domain or the frequencydomain for the construction of the resistivity and IP maps.Data Processing in the Time DomainData processing in the time domain was executed at a trans-SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 39

mitted frequency of 0.1 Hz. After stacking and filtering, weobtained the electrical field attenuation and the polarization effect.Once background field removing and secondary derivativeprocessing were complete, we calculated the micro-electrical(MRE) anomaly curve and its distribution map for eachrecorded line.The early stages of signal curves in the time domain reflectinformation on shallow layers, while the late stages of thesame curves in the time domain reflect information on deeperlayers. Therefore, a vertical cross section of relative electricalproperties of the underground area can be reconstructed foreach recorded line of the survey.The processing procedure of the data in the time domain consistsof normalizing the measured data and getting a backgroundfield, then extracting the background field from superimposeddecaying curves to get anomaly fields. The micro-anomalies areextracted and their second derivatives calculated. Finally, the resultsare normalized by calculating the MRE anomaly curves.After this process was followed for each recorded line of theBSEM survey, a target layer related MRE section was obtained.Figure 7 shows a section of micro-variations in the electricalproperty in the target reservoir.The MRE anomalies are a relative measure and have to becalibrated by means of the measured resistivity log in the transmittedwell, assuring a correspondence with the absolute valuesof resistivity in the oil and water bearing layers of the reservoir. Aresistivity log has been recorded from the surface to the reservoirdepth in one of the wells in the surveyed area; a baseline resistivitymodel was constructed for the entire surveyed volume, usingthe stratigraphy and geology of the area obtained through welldata interpolation. The MRE anomaly, calibrated in such a way,allows a resistivity map, Fig. 8, of a selected reservoir layer to beobtained. Subsequently, through Archie’s law customized to thisarea, a fluid distribution map in the same targeted layer is obtainable.Data Processing in the Frequency DomainThe signal was transmitted at 21 fundamental frequencies. Afterstacking, filtering and fast Fourier transforming, amplitudeand phase curves were obtained at 63 frequencies, which includedthe first and subsequent harmonics of the recorded signals.Laboratory tests on oil saturated rock samples show dispersioneffects, i.e., a resistivity and phase dependence on the frequencyof the EM field 5 . At low frequency ranges, oil saturationis directly proportional to rock dispersion effects: thehigher the oil saturation, the greater the observed dispersion effect.The rock dispersion at low frequencies is also known as IP.The physics forming the basis of the IP phenomenon is the formationof double-electron layers in a two-phase medium (oiland water) in porous rocks.The difference of dual frequency amplitude (D-DFA) andthe difference of dual frequency phase (D-DFP) were calculated– according to a process described in the next section. Thesedifferences are quantitatively related to the IP of the reservoirand qualitatively related to the fluid presence in the reservoir.Resistivity maps of a target reservoir layer can be producedthrough 1D constrained inversion of amplitude data at each receiverline. Similarly, 3D inversion on all the dataset producesa resistivity map through classical techniques, such as the Occamalgorithm. Both 1D and 3D Born inversions require an apriori model of resistivity, one based on the resistivity log of thesurveyed well and a geological model of the reservoir and overburdenlayers. On the other hand, to generate IP maps, a priorimodel is not required, but the IP map is more qualitative.Dual Frequency Amplitude and Phase AnomaliesFig. 7. Vertical cross section of the MRE variations of line 9, in a layer of thetarget reservoir.Fig. 8. Resistivity map of the surveyed area, relative to the A1-A2 reservoir layer.This map is obtained through data processing in the time domain.IP responses can be obtained by calculating dual frequencyamplitude (DFA) and dual frequency phase (DFP), and used topredict and evaluate the oil saturation of target reservoirs.The DFA and DFP for a single transmitting location in theborehole are calculated with the following formula:• DFA = (AMPf1-AMPf3)/AMPf1• DFP= (PHf1·f3-PHf3·f1)/(f3-f1)where AMP is the recorded signal amplitude, PH is its phase,f1 is the base frequency of the transmitted signal at whichAMP and PH are recorded, and f3 is the third harmonic of f1.DFA and DFP are calculated with the above two formulaein the case of a single transmitting source location in the borehole,which mainly reflects IP anomalies of excited site-centeredsurrounding layers. Higher oil saturations producegreater DFA and DFP anomalies. DFA and DFP curves expressanomaly variations along the survey lines.Figure 9a shows DFP curves measured along survey line 9,at the base frequency of 0.5 Hz with the transmitting antenna40 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

in the borehole at the A1 location (AB1 – right above the reservoir)and at the A2 location (AB2 – below the reservoir), respectively,Fig. 4. The values of the DFP curves on the east sideof line 9 (around receiver point 240) are greater, in relativeterms, than those on the west side (around receiver point 150)— the values of the DFP curve therefore become larger andlarger as they move eastward.It is interesting to observe that on the west side of the surveyedline, the DFP in the case of EM transmission right abovethe reservoir is greater than that in the case of transmissionbelow the reservoir, namely, |DFP AB1 |>|DFP AB2 |. Moreover,value differences between the two curves are large. On the eastside of the line this behavior is the opposite, namely, |DF-P AB2 |>=|DFP AB1 | and the differences between the two curves arerelatively small. These observations prove that there is a gradientof IP effects from west to east in the surveyed area, and IPeffects in the case of EM transmission above the reservoir, aregreater than those in the case of EM transmission below thereservoir. On the west side of line 9, the difference between polarizedtargets is large. Conversely, on the east side, dual phaseanomaly in the case of EM transmission below the reservoir isonly slightly greater than in the case of EM transmission abovethe reservoir. This observation leads to the conclusion that thedifference between polarized targets below and above thereservoir is relatively small.Figure 9b shows the DFA curve of line 9 at the base frequencyof 0.5 Hz with the transmitting antenna in the boreholeat the locations AB1 and AB2, which show similar behaviorsto those of DFP; values of the DFA curves on the east sideare greater than those on the west. In addition, the differenceFig. 9. Dual frequency curves of survey line number 9 (top: DFP, bottom: DFA)relative to two transmitting locations in the borehole (A1 = AB1, A2 = AB2).between the two curves in the west part is greater than those inthe east part. Subsequently, we observed that the DFA isgreater in the case of transmission below the reservoir (AB2)than in the case of EM transmission above the reservoir (AB1),namely, |DFA AB1 | > |DFA AB2 |. Curves in the east part show theopposite behavior, namely, |DFA AB2 | > |DFA AB1 |.In conclusion, on the west side of line 9, the IP effect isgreater in the case of EM transmission above the reservoirthan in the case of EM transmission below the reservoir,which indicates that the difference between polarized targetsbelow and above the reservoir is relatively large. On the eastside of line 9, the IP effect is greater in the case of EM transmissionbelow the reservoir than in the case of EM transmissionabove the reservoir, which indicates that the differencebetween polarized targets below and above the reservoir issmall. The same analyses were executed on all the surveyedlines. These qualitative observations demonstrated that agradient of EM properties variation was observed in the surveyedarea in the direction west to east. This gradient wasattributed to fluid saturation variations, and it was coherentwith the direction of the water injection front from west toeast in this flank of the field.Differences of Dual Frequency Amplitude and PhaseTo quantify these observations and facilitate the analysis of IPanomalies, we calculated the difference of DFA and DFP in thecase of EM transmission below and above the reservoir, introducingthe difference of D-DFA and the D-DFP with the followingformulae:• D-DFA=DFA AB2 -DFA AB1• D-DFP=DFP AB2 -DFP AB1where DFA is the dual frequency amplitude, and DFP is thedual frequency phase, AB1 represents EM transmission abovethe reservoir in location A1, Fig. 4, and AB2 represents the EMtransmission below the reservoir in location A2, Fig. 4.We observed that DFA values are positive while DFP valuesare negative. DFP anomalies in the case of EM transmissionabove the reservoir are greater than in the case of EM transmissionbelow the reservoir. DFA anomalies in the case of EMtransmission above the reservoir are smaller than in the case ofEM transmission below the reservoir.Finally, we plotted an isoline map of the D-DFA at afrequency of 0.5 Hz, Fig. 10, and an analogous map of D-DFPanomalies, Fig. 11.It is interesting to note that IP is a measurement independentfrom the absolute value of resistivity and is therefore notinfluenced by porosity variations in the surveyed layer or bythe salinity of the saturating fluid.To verify this feature of IP, the survey was repeated in thewest side of the area, transmitting at the locations A1 and A3(at the well bottom-hole, Fig. 4).D-DFA was computed for the A1 to A3 layer, and the resultis shown in Fig. 12. The layer A2 to A3, included in this survey,is constituted essentially of very tight rocks with almost noSAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 41

Fig. 10. The D-DFA at a frequency of 0.5Hz for the A1-A2 reservoir layer. Theblue/green color corresponds to water prevalence, and the yellow/red colorcorresponds to oil prevalence.Fig. 12. The D-DFA at a frequency of 0.5 Hz for the A1-A3 reservoir layer.Fig. 11. The D-DFP at a frequency of 0.5 Hz for the A1-A2 reservoir layer.oil in the porous space (see the porosity log in Fig. 4). We observedthat IP is not affected by the presence of highly resistive,tight layers, since the two maps in Figs. 10 and 12 are almostidentical.Similarly, the survey was repeated on the east side of the area,transmitting in the borehole from locations A1 and A4, and focusingthe measurement on a thin upper layer of the reservoir.The results of D-DFA are plotted in Fig. 13. Due to the high oilsaturation in this layer, the IP effect is very relevant and presentssome differences from the corresponding section in Fig. 10,which also includes the water presence in the A4 to A2 layer.Resistivity Inversion Constrained with Well Logging DataThe Occam method, a linear iteration algorithm, is used forthe 1D inversion process to obtain resistivity maps in the frequencydomain. An initial resistivity model has to be built,with clearly some influences on the inversion results. The initialmodel is based on the resistivity log measured from the surfaceto reservoir depth in a nearby well. To constrain the datain the reservoir section, we used the resistivity log directlymeasured in the BSEM transmission well. To assure the correspondenceof the resistivity layers from the surface to the bottom-hole,an accurate geological model was built, based on theinterpolated stratigraphy of about 10 wells in the surveyedarea. In the absence of this information, seismic data can beused to support the geological model. The resistivity of overburdennon-target layers is constrained by values variable inFig. 13. The D-DFA at a frequency of 0.5 Hz for the A1-A4 reservoir layer.defined ranges. The target layers are also constrained but witha wider range of resistivity from 1 ohm to 100 ohm; less than10 ohm is the range for water bearing layers, due to the highsalinity, and the range is higher for oil bearing layers.Pseudo 2D resistivity cross sections were produced after severaliterations. Subsequently, the resistivity section of the targetreservoir layer was extracted. Last, the pseudo 2D resistivitysections were deployed as an initial model to conduct the 3DBorn approximation that produced the final 3D inversion resistivitymap of the target reservoir layer.Before starting the 3D inversion, the resistivity of thewhole survey area had been constrained, with the maximum42 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

esistivity is limited to 100 ohm and the minimum resistivitylimited to 1 ohm; no other constraints, such as fluid saturationsmeasured at the well locations in the area, were appliedbecause this data was used subsequently to validate the resultsfrom the BSEM survey. Figure 14 displays a slice of thereservoir layer after 3D inversion.FLUID DISTRIBUTION CALCULATIONSOur goal was to obtain a fluid distribution map of the surveyedarea; different approaches are available to achieve this.One approach is to quantify water saturation through theclassic Archie’s law, Fig. 15, using the following equation:Fig. 14. A slice of the resistivity map in the target reservoir, originated from the3D inversion.Fig. 16. One example of an oil saturation (So%) map obtained by integratingBSEM resistivity and IP maps through artificial neural network techniques.where Sw is water saturation, Rt is fluid saturated rock resistivity,Rw is water resistivity, is porosity, m is the cementationexponent of the rock, and n is the saturation exponent.This is an approach used in open hole log processing in thisarea, but its main drawback for 2D/3D data processing residesin the required accuracy of the input parameters. Resistivitydistribution in the target reservoir was obtained throughBSEM methods, and the porosity distribution was availablethrough geological modeling with the support of seismic. Unfortunately,these data are not always available as an accuratedistribution of m, n and Rw through all the target reservoirlayers. Uncertainties in the distribution of these parameters insuch a large area could bias the results of the calculations, especiallyin carbonate reservoirs with dual or triple porositysystems, variable salinity water and imbibition/drainage mechanismslinked to the water injection/hydrocarbon productionprocess.An alternative approach is to capitalize on the different deliverablesof the BSEM method and on the direct saturationmeasurements at the wells’ locations. The previous paragraphsdescribed how BSEM survey data can be processed in alternativeways, in the time domain and in the frequency domain,producing at least five different kinds of maps of the targetreservoirs; a resistivity map in the time domain, a D-DFA map,a D-DFP map, and resistivity maps from 1D and 3D inversionsin the frequency domain. It was observed how all these mapscoherently reveal a gradient of anomalies west to east, in linewith the direction of the water injection front.Artificial intelligence neural networks were deployed to integratethe five attribute maps into one final fluid distributionmap, reflecting reservoir oil saturation, Fig. 16.The structure of the neutral network is composed of inputlayers, intermediate layers and output layers. The number ofinput layers is related to the number of input parameters; inour case, the five maps of resistivity and IP. The number of intermediatelayers is three, namely 10, 15 and 20. The numberof output layers is one. Therefore the network in our case was5x10x1, 5x15x1 and 5x20x1.A generalized gradient descent algorithm for linear adaptivewas used as a training method.The accuracy of this approach can be further increased byconstraining the model with the actual oil and water saturationdata measured at the 10 well locations in the area.CONCLUSIONSFig. 15. One example of an oil saturation (So%) map obtained through Archie’slaw. The red star corresponds to the location of the transmitting BSEM well.The first BSEM pilot field survey in Saudi Arabia was successfullycompleted. Field operations were conducted safely andsmoothly. The data quality of the recorded signals is very satisfactory.Through signals processing of the measured Er, we obtainedtwo parameters characteristic of IP: the electrical anomalyof D-DFA and D-DFP. Moreover, resistivity maps of thereservoir’s surveyed layers were obtained through data processingin a time domain and in a frequency domain (through 1Dand 3D constrained inversion). In the surveyed area of thefield, the comparison among these anomaly maps shows verysimilar distribution patterns. Oil saturation prediction wasSAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 43

then quantified from the resistivity maps by the Archie’s lawapproach. Integration of resistivity and IP maps provided thefinal output of the BSEM survey — the map of fluid distribution.The oil saturation predicted by the BSEM method wasthen compared with the net oil column thickness taken fromthe wells’ production data.Fluid distribution maps obtained with the BSEM methodare coherent with production data measured at the well locations.BSEM results are also consistent with crosswell EM findingsin a portion of the same area. The specificity of the BSEMmethod, in respect to crosswell EM, is that it requires only onesurveyed well to obtain a map of fluid distribution for a reservoirtarget layer, km away from the wellhead (up to 4 km, asdemonstrated in this pilot). Crosswell EM, in contrast, allowshigher resolution results, but it is limited to cross sections betweentwo or more wells that are close enough for EM propagation(about 1 km in open holes, less in cased holes). Thethree key R&D objectives for the BSEM survey in this field pilotwere achieved. First, results demonstrated the capability torecord at the surface an EM signal generated in the reservoir.Second, the capability of the BSEM survey to discriminate betweenoil and water saturated reservoir zones. Third, the surveysucessfully obtained resistivity maps and a fluid distributionestimate plausible and coherent with the informationobtained from well logs, production data and reservoir models.ACKNOWLEDGMENTSThe authors are thankful to Saudi Aramco and BGP managementfor their permission to publish this article. Special gratitudegoes to Abdulaziz O. Al-Kaabi, Jiang Lianbin, NiuYongjian and Zhang Rujie for their continuous support. Similarthanks go to Duffy Russell for the geological characterization,to the reservoir and geophysical technology teams ofEXPEC ARC, and to Hamad Al-Marri, Abdulrahman Al-Mulhim,Suliman Al-Suwailem and Walid Al-Guraini for field engineeringand operations.This article was presented at the SPE Annual TechnicalConference and Exhibition, Denver, Colorado, October 30 -November 2, 2011.REFERENCES1. Marsala, A.F., Al-Buali, M., Ali, Z., et al.: “First Pilot ofBorehole to Surface Electromagnetic in Saudi Arabia: ANew Technology to Enhance Reservoir Mapping andMonitoring,” EAGE I005 extended abstract, presented atthe 73 rd European Association of Geoscientists and EngineersConference and Exhibition, Vienna, Austria, May 23-26,2011.2. Al-Harbi, A.A., Schmitt, D.P. and Ma, S.: “TowardQuantitative Remaining Oil Saturation (ROS):Determination Challenges and Techniques,” SPE paper147651, presented at the SPE Annual Technical Conferenceand Exhibition, Denver, Colorado, October 30 - November2, 2011.3. He, Z., Liu, X., Qiu, W. and Zhou, H.: “MappingReservoir Boundary by Borehole Surface TFEM: Two CaseStudies,” The Leading Edge, Vol. 24, No. 9, September2005, pp. 896-900.4. He, Z., Hu, W. and Dong, W.: “Petroleum ElectromagneticProspecting Advances and Case Studies in China,” Surveysin Geophysics, Vol. 31, No. 2, Spring 2010, pp. 207-224.5. Zhdanov, M.S.: Geophysical Electromagnetic Theory andMethods, textbook, Amsterdam, The Netherlands: Elsevier,2009.6. Xiao, Z.S., Xu, S.Z., Luo, Y.Z., et al.: “Study onMechanisms of Complex Resistivity Frequency DispersionProperty of Rocks,” Journal of Zhejiang University (ScienceEdition in Chinese), Vol. 33, No. 5, 2006, pp. 584-587.7. Marsala, A.F., Ruwaili, S., Ma, S.M., et al.: “CrosswellElectromagnetic Tomography: From Resistivity Mapping toInterwell Fluid Distribution,” IPTC paper 12229, presentedat the International Petroleum Technology Conference,Kuala Lumpur, Malaysia, December 3-5, 2008.BIOGRAPHIESDr. Alberto F. Marsala has more than20 years of oil industry experience. Forthe last 5 years, he has been working inSaudi Aramco’s Exploration andPetroleum Engineering Center – AdvancedResearch Center (EXPEC ARC).His main interests are in reservoir technologies,deep diagnostics, electromagnetic methods andreservoir characterization while drilling via measurementson cuttings. Previously while at Eni and Agip, Albertoparticipated in several upstream disciplines, including 4Dseismic, reservoir characterization, petrophysics, geomechanics,core analysis, drilling and construction in environmentallysensitive areas. He worked on the TechnologyPlanning and R&D Committee of Eni E&P. Alberto wasHead of Performance Improvement for the KCO joint venture(Shell, ExxonMobil, Total and others) concerned withthe development of giant fields in the northern Caspian Sea.He has authored a book, several technical papers andinternational patents. Alberto served for several years onthe Board of Directors of the Society of PetroleumEngineers (SPE) - Italian Section, and he is currentlyQuality System Manager of the European Organization forQuality.In 1991, Alberto received his Ph.D. degree in NuclearPhysics from the University of Milan, Milan, Italy, and in1996, he received an MBA in Quality Management fromthe University of Pisa, Pisa, Italy. He also holds aSpecialization in Innovation Management, received in 2001.44 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Muhammad H. Al-Buali joined SaudiAramco in 2002. He is a Petroleum Engineercurrently working as the AssistantSuperintendent in the SouthernArea Gas Well Completion OperationsDepartment. Muhammad has 9 yearsof oil industry experience, mainly inproduction optimization and well intervention.In 2002, he received his B.S. degree in Applied ChemicalEngineering from King Fahd University of Petroleum andMinerals (KFUPM), Dhahran, Saudi Arabia.Zaki A. Ali is a Senior Petroleum Engineerwith the Southern Area’s‘Udhailiyah Reservoir ManagementDivision. Prior to joining Saudi Aramcoin 1987, he worked for the Ministry ofPetroleum and Minerals for 4 years.Zaki received both his B.S. and M.S.degrees in Petroleum Engineering from King Fahd Universityof Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia.Dr. Shouxiang M. Ma is a PetrophysicsConsultant in the Reservoir DescriptionDivision, on assignment to the UpstreamProfessional Development Centeras the Upstream PetrophysicsProfessional Development Advisor. Heis a mentor for the Petroleum Engineering(PE) Advanced Degree Program and the PE TechnologistDevelopment Program (TDP), and he also is a TDP TechnicalReview Committee member representing Petrophysics.Mark actively serves as the Petroskills Petrophysics CurriculumAdvisor, representing Saudi Aramco. Before joiningSaudi Aramco in 2000, he worked as a Lecturer atChangjiang University, Jingzhon City, China, and as a LabPetrophysicist at the New Mexico Petroleum RecoveryResearch Center, the Wyoming Western Research Instituteand Exxon’s Production Research Company.Mark received his B.S. degree from China University ofPetroleum, Beijing, China and his M.S. and Ph.D. degreesfrom the New Mexico Institute of Mining and Technology,Socorro, NM, all in Petroleum Engineering.He is a member of the Society of Core Analysts and theSociety of Petroleum Engineers (SPE) and he serves on theSPE’s Formation Evaluation Award Committee andAIME/SPE Robert Earll McConnell Award Committee.Mark has more than 50 publications in petrophysics. Hewas awarded the 2011 SPE Saudi Arabia Section ActiveTechnical Involvement Award and is a technical reviewerfor the journals SPERE&E and PS&E.Dr. Zhanxiang He is the Professor ofEngineering, Deputy Director of theProfessional Committee of Electromagnetismin the Chinese Geophysical Society,Chief Engineer of BGP (EasternGeophysical Company), and Directorof the R&D Center in the IntegrativeGeophysical and Geochemical Division.He is a member of the Professional Committee of Explorationin the Chinese Geophysical Society and of the Societyof Exploration Geophysicists (SEG).Tang Biyan has been with BGP since1996, working in the Non-Seismic Department.He is in charge of R&D forcontrol source electromagnetic (CSEM)prospecting. Tang designed four sets ofCSEM instrument systems and draftedthe borehole to surface electromagnetic(BSEM) and time frequency electromagnetic (TFEM) prospectingoil industry standard in China. He was awarded the secondprize for new technology development from the China NationalPetroleum Corporation in 2006 and the first prize for CSEMtechnology development from BGP in 2007.In 1996, Tang received his M.S. degree in GeophysicalProspecting from Chengdu Technique University,Chengdu, China.Guo Zhao has been with BGP since2005, and he works in the Non-SeismicDepartment, specifically in theR&D for control source electromagnetic(CSEM) prospecting. His previousexperience includes work in geologicaland integrated drill logging.Guo received his B.S. degree from Chongqing PetroleumCollege, Chongqing, China in 1998. He then went on toreceive his M.S. degree in Geophysical Prospecting andInformation Technology from the China University ofPetroleum, Beijing, China, in 2005.Tiezhi He has been with BGP since1998. He works in the Non-SeismicDepartment where he is in charge ofprocessing and interpretation of CSEMprospecting data. Tiezhi completed theprocessing and interpretation of thetime frequency electromagnetic(TFEM) and borehole to surface electromagnetic (BSEM)data from a section of more than 4,000 km, increasing hisexperience in these areas.In 1998, he received his degree from the ChangchunGeological College, Changchun, China.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 45

Toward Quantitative Remaining OilSaturation (ROS): Determination Challengesand TechniquesAuthors: Ahmed A. Al-Harbi, Dr. Denis P. Schmitt and Dr. Shouxiang M. MaABSTRACTRemaining oil saturation (ROS) is a crucial input to reservoirdevelopment projects. Determination of this time-dependentparameter assists the evaluation of the sweep efficiency,provides calibration points for simulation models, and sets thebasis for future workover and enhanced oil recovery (EOR)projects.Quantification of ROS in waterflooded areas has alwaysbeen a known challenge for resistivity-based techniques. Themixed water salinity environment, due to differences betweenoriginal connate water and injected water, and the impact ofthe imbibition process on the saturation exponents are themain hindrances.Over the past few years, data acquisition strategies havebeen deployed to overcome these challenges. The strategies includein-situ measurements that are insensitive to water salinityand to fluid displacement processes, such as nuclear magneticresonance (NMR) in log-inject-log (LIL) mode, carbon/oxygen(C/O) and dielectric logs. Extensive wireline formation testing(WFT) programs usually follow to confirm the findings fromthese measurements. Depending on the well’s criticality, specialcoring programs might also be included, such as sponge coringor liquid trapping. Special planning and standardization effortsare necessary for such extensive data acquisition programs toensure data quality and consistency.This article presents Saudi Aramco’s data acquisition strategiesfor ROS applications. It also highlights the main challengesand the technologies deployed to resolve them. The advantagesand limitations of these technologies are reviewed.Finally, our ongoing efforts to reduce the uncertainties of suchanalyses are presented.INTRODUCTIONFor half a century, remaining oil saturation and residual oilsaturation have been extensively reviewed in the literature witha reasonable consensus on their definitions. Yet, the acronymROS has been interchangeably used, whenever referring to eitherof them. For consistency, in this article, the authors definethe ROS acronym as “remaining oil saturation,” and use theSOR acronym for “residual oil saturation” (data points on thesaturation axis of the capillary pressure and/or relative permeabilitycurves) after waterflooding, which is in line with previouspublications 1-3 . ROS is the total oil saturation, at a givenpoint in time and space, of a reservoir under going displacementof oil by water. While a portion of this oil is in a mobilephase and its recovery is time dependent, the other portion isin an immobile phase, i.e., residual oil, and its recovery requiresa game changer, such as chemical enhanced oil recovery(EOR). SOR is the oil saturation at which relative permeabilityto oil vanishes; therefore, it is trapped by invading water. Notionally,the value of ROS can be as low as that of SOR, butusually it is greater.At the microscopic scale, viscous and capillary forces are themain opposing forces governing any immiscible displacementprocess. Displacement processes are carefully engineered tomaximize viscous forces while, if applicable, minimizing capillaryforces to enhance displacement efficiency and therefore oilrecovery. These forces are largely dictated by interrelated reservoirattributes of wettability, pore structure and fluid dynamics.Much research has been done to investigate the fluids distributionand displacement mechanisms, the interplayingnatures of these attributes, and the forces at work on the microscopiclevel 4, 5 .At the macroscopic level, reservoir structure and features,e.g., faults, fractures, barriers, strata forms and heterogeneities,make reservoirs geologically complex. Coupled with production/injectionwell fluid flow dynamics and the effects of gravity,these multidimensional complexities dominate fluid movementwithin the reservoir, and therefore result in complex fluiddistributions, both vertically and laterally. A diagrammaticdemonstration of the displacement process at both scales: microscopicand macroscopic, is presented in Fig. 1.Quantitative knowledge of the spatial fluid distribution is akey input in the decision making process of reservoir managementand is essential for proactively enhancing the sweep efficiencyand water front conformance. It also defines the opportunitywindow for future EOR projects, which can beeconomically marginal. A high degree of accuracy when calulatingremaining oil distribution is highly desirable. For all ofthese reasons, there is a need for deployment of data acquisitionstrategies and best practices at desirable frequencies andlocations, not only at the mature stage, but also at the earlyproduction life of the field.46 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

RESISTIVITY-BASED MODELSGenerally, resistivity-based saturation models, e.g., Archie andother non-Archie models, have been the preferred techniquesfor water saturation calculations because of their reliability,abundance, deeper depth of investigation and cost effectiveness.In using resistivity measurement for reservoir surveillance,the main factors that affect the final results are saturationexponent and formation water salinity.Drainage-Imbibition Resistivity HysteresisLike other multiphase reservoir petrophysical properties, suchas capillary pressure and relative permeability, resistivity measurementis displacement process dependent, i.e., it displays adrainage-imbibition hysteresis phenomenon. Interpretationof resistivity logs with drainage-imbibition hysteresis can bechallenging.First, it is difficult to recognize intervals where an imbibitionprocess has started. This is especially true for newlydrilled wells in flooded areas. Logs in existing observationwells will be relatively easier to interpret if a good baseline resistivitylog is available for time lapse analysis.Second, due to this drainage-imbibition hysteresis, as studieshave demonstrated 11, 12 , the saturation exponent used in saturationcalculations for drainage is different from that used forimbibition. Historically, electrical rock properties measured byoperating companies in their own laboratories or in commerciallaboratories are mostly drainage saturation exponent data.Compared to drainage saturation exponent data, imbibitionsaturation exponent data is rarely available and is much moredifficult and time consuming to obtain.Fig. 1. Saturation Condition 1 (in A, B and C) is the initial oil saturation before experiencingany contact with injected or aquifer water; this represents the maximumrange for ROS. Condition 2 is oil saturation just ahead of the water front withsome water saturation increase (depending on reservoir wettability and the appliedrate of waterflooding) by spontaneous imbibitions. Condition 3 is at the residualoil saturation, i.e., all movable oil has been displaced with water; therefore ROSequals SOR. At the macroscopic scale, reservoir features, such as fractures or barriers,may result in bypassed oil zones/intervals, areas that were never in contactwith water and yet are behind the water front.A result of the ROS significance is a spectrum of determinationtechniques that has been deployed over the past 40 yearsand extensively reviewed 1, 2, 6-10 . Determination techniques canbe summarized as: (1) material balance, (2) core analysis, (3)single well tracer tests, (4) pressure transient tests, and (5)wireline logging techniques. The techniques in the wireline loggingcategory, which are the focus of this article, include resistivity,pulsed-neutron capture, pulsed neutron spectral (carbon/oxygen(C/O)), nuclear magnetic resonance (NMR) anddielectric methods. This article puts more emphasis on theNMR and dielectric acquisition for ROS determination applications,and it highlights the main advantages and challengesof these technologies, with field examples from carbonatereservoirs.Mixed Formation Water SalinityFormation water salinity or resistivity is one of the key parametersthat can affect resistivity derived saturation. In virginreservoirs, formation resistivity is high, thereby dominatingsaturation calculations. In parts of a reservoir that have beenswept by water, formation resistivity can be reduced dramaticallyand the accuracy of formation water salinity/resistivitybecomes significant. In cases where imbibed water salinitiesdiffer from the salinities of the original formation water (connatewater), we have the challenge of a typical mixed salinityproblem. This salinity contrast might be high enough to erroneouslymake a water zone look like an oil zone if nonrepresentativesalinities are used. A reservoir’s long production andinjection history, and complex geology make salinity profileshard to predict.To improve the accuracy of reservoir surveillance, distributionof formation water salinity in space and time is required.It has been done by extensive water sampling programs. In thenear future, a salinity logging tool can serve this purpose 10 .A log-inject-log (LIL) procedure was devised 7 in an attemptto increase the reliability of resistivity-based measurements ofoil saturation. The special procedure involves running a baseSAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 47

esistivity log, Rt(Sw), followed by several injection steps withsolvent to miscibly displace all remaining oil and then injectingformation water with a known resistivity, Rw, and finally re-loggingthe resistivity, Rt(Sw=1)=Ro. With this procedure, formationwater salinity information is available, but not necessarilyneeded. The saturation exponent is needed, as is informationon how it might be altered after the injection process. The accuracyof this procedure heavily relies on the effectiveness ofthe injection process and the miscible displacement of the remainingoil, which can be quite challenging.NUCLEAR MAGNETIC RESONANCE (NMR)NMR technology was first introduced for ROS application 13, 14 .The main advantage of NMR is the independence it offers fromwater salinity and saturation exponent. The NMR technologymeasures relaxation times, which tend to overlap the oil andwater signals in a pore space, hindering any identification or obviouslyany quantification efforts, Fig. 2a. Therefore, Robinsondeveloped a procedure that results in splitting the water signalfrom the oil signal in the relaxation time domain, allowingquantification of remaining oil 13, 14 , Fig. 2b.This procedure is what is known in the industry as doping orLIL 8, 15, 16 . A chemical material, manganese chloride (MnCl 2 ),is added to the drilling fluid before drilling the zones of interest.When the MnCl 2 invades the formation during the drillingprocess, the paramagnetic ions, Mn ++ , dissolve only in water,which shortens its relaxation time, shifting it away from that ofoil, which remains unchanged. Concentration of the dopantshould be carefully designed to widen the split between oil andwater just enough but not cause a relaxation time so short thatit goes beyond the tool’s detection limits.A relaxation time cutoff is later applied to quantify the volumesof water and oil. Likewise, the injection procedure forthe resistivity-based approach, the ROS accuracy from theNMR approach is determined by the success of the dopant injectionin splitting the water and oil signal.DIELECTRIC TECHNOLOGYDielectric technology has had a long history of developmentFig. 2. Typical NMR measurements before and after doping. Plot A shows NMRrelaxation time before doping, on the base log, with a given pore size distributionand irreducible water information; the oil and water signal cannot be differentiatedas they tend to overlap. Plot B demonstrates the effect of the doping process on theNMR signal: with a clear shift in water signal from the oil signal the oil volumecan be computed after applying a simple cutoff.since its early introduction 17 . Over the years, dielectric technologyhas advanced, following technological developments in itshardware and software. The capabilities and limitations of thetechnology have been improved in the new generation of dielectrictools 18 .Similar to NMR technology, the dielectric logging providesan answer that is independent of water salinity and saturationexponent. Additionally, it is a direct measurement; no specialprocedure is required prior to logging, i.e., doping for theNMR. It measures water-filled porosity near the wellbore. Thedielectric measurement is based on the electromagnetic wavepropagation in porous media that is governed by conductivityand dielectric permittivity. The dielectric permittivity of waterstands out among any other common minerals or fluids in petroleumsystems, Table 1. This physical phenomenon featuresthe dielectric measurements to be sensitive only to the aqueousphase. Therefore, coupled with total porosity information andmatrix permittivity, oil saturation in invaded zones can be calculated18 .FIELD EXAMPLESThis section looks at field examples from Saudi Aramco fieldsthat are undergoing waterflooding recovery. Over the years,water with different salinities have been injected for pressuremaintenance. Together with the original formation water, allhave resulted in a mixed salinity environment ranging from230 kppm to as low as 10 kppm. The subject reservoirs arecarbonates, mainly limestone with localized dolomitization.The reservoir quality ranges from highly porous and permeablegrainstones at the top, degrading downward to tight faciesmudstones and wackestones at the bottom. In the followingexamples, all the wells are located behind waterfronts, inswept areas, and near water injectors.Well #1The LIL procedure was followed. The well was drilled with a8½” bit and conventional mud across the interest zone, andMaterialQuartz 4.4Sandstone 4.65PermittivityLimestone 7.5 to 9.2Dolomite 6.8Clay (dry colloids) 5.0 to 5.8Anhydrite 6.4Halite 5.9Gypsum 4.16Oil 2.2Air, Gas 1Water* 50 to 78Table 1. Reservoir mineral and fluid relative dielectric permittivity*Dependent on frequency, pressure, temperature and salinity.48 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

sponge cored. This hole section was then logged with a triplecombo for basic formation evaluation, and NMR determinedpore size distribution. Subsequently, the hole was underreamedto a 10” size with mud doped with MnCl 2 at a concentrationof 1.59 lb/bbl. The underreaming step is essential to removethe mud cake and allow the dopant to invade near thewellbore area and mix with the water. The mud density wascarefully designed to allow slightly overbalance drilling, typicallynot more than 500 psi, to avoid stripping the oil from thenear wellbore region. After enlarging and doping the well,NMR was run again. Figure 3 shows the relaxation time, T2,before and after doping. A clear shift in the water signal can beseen in the “after doping” relaxation time. A T2 cutoff was applied,and remaining oil volume and saturation were computed.A subsequent wireline formation tester (WFT) run indicateda water pressure gradient, and water samples werecollected. In this well, the ROS values from NMR are probablynear the SOR values, Condition 3 in Fig. 1. Sponge core measurements,which later became available, confirmed the previousfinding from NMR and WFT. Additionally, collected watersamples by WFT were analyzed at the laboratory for salinitymeasurements. This salinity data was used later on to refinethe resistivity-based saturation calculation. Water salinity informationis also useful for understanding the regional fluid’sdisplacement, and it can also to be used as a best estimate forfuture wells and as input for resistivity-based saturation calculation.Well #2Almost the same procedure used in Well #1 was followed inWell #2, except the hole size; here it was drilled at 5 7 ⁄8” andthen underreamed to 8 3 ⁄8”. Following the formation evaluationand ROS determination from NMR, a C/O log was run underflowing condition across the open hole. The flowing conditionis necessary to minimize potential fluid reinvasion effects onthe C/O logs 19 . In Fig. 4, the ROS determined from the C/Olog compared well with its counterpart from the NMR LILlog. ROS determined from laboratory tests of the sponge corealso showed good comparison with that from the NMR andC/O logs. In the area around this well, not much displacementis expected as the ROS values are probably very close to theSOR values, showing excellent sweep near the injectors.Well #3In this well, the new dielectric technology was utilized for ROSapplication and compared with NMR-LIL results. The dielectriclog was run before and after doping to ensure no side effectson the dielectric measurements. Figure 5 shows a compositeplot of the ROS from the NMR-LIL and dielectricmeasurements. Dielectric technology measures the water filledporosity, which is then displayed alongside total porosity takenfrom formation evaluation analysis. The ROS was then computedfrom the NMR and dielectric measurements, andshowed a very good match.Well #4Fig. 3. Track #4 shows the NMR relaxation time, T2, before doping; note the oiland water signals overlap at the long T2. After doping, T2 is shown in Track #3;the water signal is shortened and shifted to the left, splitting from the oil signal.The T2 cutoff (in magenta color) was applied, allowing the remaining oil andwater volumes computation in Track #2. Track #1 shows a comparison betweenROS from NMR and from sponge core measurements.Instead of the LIL procedure, the inject-log (IL) procedure wasfollowed. IL is an optimized program in which the open holesection is drilled across the target zone with the doped mud,eliminating the need for a subsequent underreaming run withthe dopant and the NMR relogging part. It saves rig time andSAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 49

also reduces associated logging costs. But with IL, pore sizedistribution and irreducible water information from NMR isno longer available anymore as the NMR relaxation time isaffected by the dopant.A triple combo, using NMR, dielectric logging and WFTtools, was run for the ROS evaluation in this region of thefield. Unfortunately, the doping job was not effective, asshown in the NMR relaxation time, Fig. 6. The desirable shiftof the water signal in the relaxation time domain was notachieved. This poor doping hindered the ROS evaluation fromNMR, as it is very difficult to choose a relaxation time cutoffwithout any apparent separation between the oil and watersignals. A cutoff from previous experience in nearby wellssharing a similar environment might be used, but it will onlyallow qualitative evaluation. It is also worth mentioning thatFig. 4. Track #3 shows the NMR relaxation time, T2, after doping. NMR measurementsof the remaining oil and water volumes are shown in Track #2. Track #1shows a comparison between the ROS from NMR and C/O logs, and the ROSfrom sponge core measurements.Fig. 5. Track #5 and #4 show the NMR relaxation times, T2, before and after thedoping. NMR and dielectric measurements of the remaining oil and water volumesare displayed in Tracks #3 and #2, respectively. Track #1 shows a comparisonbetween the ROS from NMR and the ROS from dielectric log.50 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

oil volumes. The last track shows the ROS profile based on thedielectric log.A subsequent WFT run was conducted for pressure testsand downhole samples. The WFT pressure gradient and samplesconfirmed the excellent bottom-up sweeping, with an oilcolumn at the top of the reservoir, that was anticipated basedon regional experience and simulation effort. Additionally, watersamples collected by WFT were analyzed at the lab forsalinity measurements and then used to re-compute the resistivity-basedsaturation.This example shows the business impact of utilizing thedielectric technology to compute ROS. It improves the overallaccuracy in determining such a critical reservoir parameter.Also, cost avoidance is realized when deploying dielectric technology,which eliminates the need for the lengthy dopingprocedure and all associated rig time and logging costs.SAUDI ARAMCO ROS DATA ACQUISITION STRATEGIESSaudi Aramco has a long history of establishing long-term dataneeds early in a reservoir’s life cycle. The ROS determinationbest practices and fit-for-purpose data acquisition strategies describedhere are deployed for optimum reservoir management.Periodic observation wells are planned and executed with specialcare.LocationObservation wells must be carefully located across the fieldand behind the waterflood front in already swept or stillsweeping areas. As the waterfront advances, new observationwells must be planned accordingly to track the waterfloodfront advancement.Logging ProgramFig. 6. Track #3 shows the NMR relaxation times, T2, after the doping. Poordoping is obvious as the NMR shows no apparent separation between the oil andwater signals. Tracks #2 and #1 show the ROS computed from dielectric logging.this is an irreversible process; once the hole is drilled and mudcake is formed, there is no practical way to go back and redopethe well.In this job, the dielectric measurement, in conjunction withthe triple combo logs, helped in achieving the objective of thiswell and provided a vertical ROS evaluation. Figure 6 showsthe volume of water computed by dielectric methods togetherwith the total porosity log from an open hole analysis, in thiscase based on density-neutron logs highlighting the remainingTriple combo logs are almost always run for basic formationevaluation and ROS estimation. For ROS determination the dielectric,NMR (in LIL or IL modes) and pulsed neutron (sigmaand/or C/O) techniques are the main in-situ technologies, withmore focus lately on the dielectric method for its use in reservoircharacterization and cost effectiveness. All these logs (dielectric,NMR and pulsed neutron) are shallow measurements(a few inches) compared to the deep resistivity logs (up to 10ft). Steps should be taken to make these measurements as reservoirrepresentative as possible. Drilling mud systems should bedesigned to allow minimal overbalance drilling to avoid anystripping of remaining or residual oil.WFT ProgramBecause pressure gradient analysis and downhole samples areconsidered a direct measurement of reservoir fluid, they areused to confirm and validate open hole log analysis. Samplesare more conclusive than pressure gradient analysis in reservoirswith a depletion process under way. At a given ROSvalue, WFT that is flowing oil suggests that the ROS is definitelyhigher than the SOR for this particular zone. On theSAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 51

contrary, WFT that is flowing only water is indicative that thiszone might be near SOR values; this would need to be confirmedwith SOR values from special core analysis. In addition,water samples are also collected to firm up the salinity valuesused in the log interpretation.Saturation MonitoringThe established ROS profiles from open hole logs are used as abaseline for later reservoir monitoring (either resistivity orpulsed neutron) to track vertical fluid encroachment andsweeping efficiency.SimulationThese dynamic ROS profiles are reliable calibration points forthe simulation models created during the history matchingprocess. Matching these points in time and space gives additionalconfidence in the models’ forecast, therefore reliablyhighlighting potential areas for near future development.CONCLUSIONSROS is a crucial reservoir attribute for accurate sweep efficiencyevaluation, reservoir development strategies and futureEOR feasibility studies. Quantitative determination of such animportant parameter requires special attention.Conventional resistivity-based techniques are challenging,especially in a mixed salinity environment. Extensive fluidsampling and imbibition of special core analysis data are requiredfor a more reservoir representative evaluation.Pulsed neutron logs are typically designed for reservoirmonitoring. Sigma is, however, sensitive to formation watersalinity, and C/O logging has a very slow logging speed.NMR LIL or IL may provide a reliable ROS evaluation insuch a mixed salinity environment; however, it is an indirecttechnique requiring the doping procedure. The quality of theNMR ROS evaluation is controlled by the quality of dopingprocess.With recent developments, dielectric logging has shown thepotential to be a reliable and cost-effective technology for ROSapplication, in addition to its use in reservoir characterization.All the above techniques can be used only to estimate themagnitude of ROS; they cannot measure the ability for oil toflow. Integrating such determined ROS with WFT samplingprovides an evaluation of ROS with respect to SOR.ACKNOWLEDGMENTSThe authors would like to thank Saudi Aramco managementfor the permission to publish this article. We also extend ourappreciation to the Reservoir Description and SimulationDepartment for their continuous support on ROS data acquisitionand field studies. Thanks are also due to other SaudiAramco organizations including the Reservoir Management,Reservoir Characterization and EXPEC ARC Departments fortheir active collaboration.This article was presented at the SPE Annual TechnicalConference and Exhibition, Denver, Colorado, October 30 -November 2, 2011.REFERENCES1. Thomas, E.C. and Ausburn, B.E.: “Determining Swept-Zone Residual Oil Saturation in a Slightly ConsolidatedGulf Coast Sandstone Reservoir,” Journal of PetroleumTechnology, Vol. 31, No. 4, April 1979, pp. 513-524.2. Thomas, E.C., Richardson, J.E., Shannon, M.T. andWilliams, M.R.: “The Scope and Perspective of ROSMeasurement and Flood Monitoring,” Journal ofPetroleum Technology, Vol. 39, No. 11, November 1987,pp. 1398-1406.3. Rafie, M.Y. and Youngblood, W.E.: “Advances inQuantitative Reservoir Description and Monitoring inSaudi Arabia,” SPE paper 22234, presented at the 12 thWorld Petroleum Congress, Houston, Texas, April 26 -May 1, 1987.4. Melrose, J.C. and Brandner, C.F.: “Role of Capillary Forcesin Determining Microscopic Displacement Efficiency forOil Recovery by Waterflooding,” Journal of CanadianPetroleum Technology, Vol. 13, No. 4, October-December1974, pp. 54-62.5. Morrow, N.R.: “Interplay of Capillary, Viscous andBuoyancy Forces in the Mobilization of Residual Oil,”Journal of Canadian Petroleum Technology, Vol. 18, No.3, July-September 1979, pp. 35-46.6. Murphy, R.P. and Owens, W.W.: “The Use of SpecialCoring and Logging Procedures for Defining ReservoirResidual Oil Saturations,” Journal of PetroleumTechnology, Vol. 25, No. 7, July 1973, pp. 841-850.7. Murphy, R.P., Owens, W.W. and Dauben, D.L.: U.S. PatentNo. 3,757,575, “Well-Logging Method,” 1973.8. Wyman, R.E.: “How Should We Measure Residual OilSaturation?” SPE paper 7182, presented at the SPE RockyMountain Regional Meeting, Cody, Wyoming, May 17-19,1978.9. Chang, M.M., Maerefat, N.L., Tomutsa, L. andHonarpour, M.M.: “Evaluation and Comparison ofResidual Oil Saturation Determination Techniques,” SPEFormation Evaluation, Vol. 3, No. 1, March 1988, pp.251-262.10. Ma, S.M., Al-Hajari, A.A., Berberian, G. andRamamoorthy, R.: “Cased Hole Reservoir SaturationMonitoring in Mixed Salinity Environments – A NewIntegrated Approach,” SPE paper 92426, presented at theSPE Middle East Oil and Gas Show and Conference,Manama, Bahrain, March 12-15, 2005.11. Al-Kaabi, A.U., Mimoune, K. and Al-Yousef, H.Y.:“Effect of Hysteresis on the Archie Saturation Exponent,”52 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

SPE paper 37738, presented at the SPE Middle East OilShow and Conference, Manama, Bahrain, March 15-18,1997.12. Ma, S.M., Al-Hajari, A.A., Kelder, O., Al-Muthana, A.S.,Srivastava, A. and Ramamoorthy, R.: “DynamicPetrophysics - Applications of Time-Lapse ReservoirMonitoring in Saudi Arabia,” SPE paper 95882,presented at the SPE Annual Technical Conference andExhibition, Dallas, Texas, October 9-12, 2005.13. Robinson, J.D., Loren, J.D. and Higdon, W.T.: U.S. PatentNo. 3,657,730, “Method for Determining ResidualHydrocarbons Present in a Subterranean EarthFormations,” 1972.14. Robinson, J.D., Loren, J.D., Vajnar, E.A. and Hartman,D.E.: “Determining Residual Oil with the NuclearMagnetism Log,” Journal of Petroleum Technology, Vol.26, No. 2, February 1974, pp. 226-236.15. Clerke, E.A., Hartman, D.E., Horkowitz, J.P., Coates,G.R. and Vinegar, H.J.: “Residual Oil SaturationMeasurements in Carbonates with Pulsed NMR Logs,”The Log Analyst, Vol. 38, No. 2, March-April 1997, pp.73-83.16. Hassoun, T.H., Zainalabedin, K. and Minh, C.C.:“Hydrocarbon Detection in Low-Contrast Resistivity PayZones, Capillary Pressure and ROS Determination withNMR logging in Saudi Arabia,” SPE paper 37770,presented at the SPE Middle East Oil Show andConference, Manama, Bahrain, March 15-18, 1997.17. Calvert, T.J. and Wells, L.E.: “ElectromagneticPropagation - A New Dimension in Logging,” SPEpaper 6542, presented at the SPE California RegionalMeeting, Bakersfield, California, April 13-15, 1977.18. Schmitt, D.P., Harbi, A.A., Saldungaray, P., Akkurt, R.and Zhang, T.: “Revisiting Dielectric Logging in SaudiArabia: Recent Experiences and Applications inDevelopment and Exploration Wells,” SPE paper 149131,presented at the SPE/DGS Saudi Arabia Section TechnicalSymposium and Exhibition, al-Khobar, Saudi Arabia,May 15-18, 2011.19. Kelder, O., Al-Hajari, A.A. and Crary, S.: “Borehole FluidReinvasion Effects on C/O Logs in Open HoleCompletion,” SPE paper 102543, presented at the SPEAnnual Technical Conference and Exhibition, SanAntonio, Texas, September 24-27, 2006.BIOGRAPHIESAhmed A. Al-Harbi is currently a SeniorPetrophysicist with Saudi Aramco’sReservoir Description and SimulationDepartment. Since joining SaudiAramco in 2003, he has held severaltechnical and supervisory positions inthe following areas: Exploration, Oiland Gas Development, and Petrophysical Special Studies, aswell as the Event Solution multidisciplinary reservoir studiesunits. Formation evaluation is Ahmed’s area of interest withan emphasis on NMR and ROS applications. Prior to joiningSaudi Aramco, he was a Senior Field Engineer with SchlumbergerWireline Logging.In 1998, Ahmed received a B.S. degree in ElectricalEngineering from King Fahd University of Petroleum andMinerals (KFUPM), Dhahran, Saudi Arabia, and in 2006,he received a M.S. degree in Petroleum Engineering fromthe Imperial College, London, U.K.Ahmed has several articles to his credit and has been amember in several technical and organizing committees ofprofessional societies and events.Dr. Denis P. Schmitt joined SaudiAramco in April 2009 as a PetroleumEngineer Consultant, focusing onacoustic logging and dielectric methods.In 1978, he received his first degree,D.E.U.G. in Sciences of the Structure ofMatters from the Université Paris XI,Paris, France. Denis went on to earn four more degrees inGeology and Geophysics before earning his Ph.D. degree inEarth Sciences and Geophysics from the University ofGrenoble, Grenoble, France, in 1985.Dr. Shouxiang Mark Ma is a PetrophysicsConsultant in the Reservoir DescriptionDivision; on assignment to theUpstream Professional DevelopmentCenter as the Upstream PetrophysicsProfessional Development Advisor. Heis a mentor for the Petroleum Engineering(PE) Advanced Degree Program and the PE TechnologistDevelopment Program (TDP), and also a TDP Technical ReviewCommittee member representing Petrophysics.Mark received his B.S. degree from China Petroleum University,his M.S. and Ph.D. degrees from the New Mexico Instituteof Mining and Technology, Socorro, NM, all in PetroleumEngineering.He is a member of the Society of Core Analysts and theSociety of Petroleum Engineers (SPE).Mark has more than 50 publications in petrophysics. Hewas awarded the 2011 SPE Saudi Arabia Section ActiveTechnical Involvement award, and is a technical reviewer forthe Journals of SPERE&E and PS&E.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 53

Utilizing Seismic Derived Rock Properties forHorizontal Drilling in the Arabian GulfAuthors: Sean Rahati, Hussain M. Al-Otaibi and Yousef M. Al-ShobailiABSTRACTThe northern offshore stringer sands contain significant oil andgas in-place, yet achieving the goal of imaging these sands hasbeen challenging. The sands and shales do not have a sufficientacoustic impedance contrast to appear in normal incident seismicdata, and the sand distribution is too erratic and complex to bepredicted with subsurface stratigraphic techniques. “Simultaneousoffset inversion” was used to separate sand from shales in the prestackdomain based on elastic rock properties. The results haveallowed us for the first time to target the sands for hydrocarbonrecovery via geosteering horizontal wells to maximize reservoircontact. Furthermore, the underlying massive main interval ofsand is better delineated, and our results show structural andstratigraphic events that were encountered during drilling, butnever until now predicted. Given the ongoing extensive horizontaldrilling program, these results will have a significant impact onthe overall drilling and production efficiency.GEOLOGIC SETTINGFig. 2. Main stratigraphic units and their typical gamma ray profiles for theNorthern offshore area of Saudi Arabia.The field is located in the Arabian Gulf, approximately 149 miles(240 kilometers) north of Dhahran, Saudi Arabia, Fig. 1. Thereservoir is of the Middle Cretaceous Period and conformablyoverlies the Lower Cretaceous regional carbonates. The reservoirwas deposited by a fluvial-dominated deltaic system that progradedover the regional’s shallow marine carbonate platform.The clastic reservoir of interest is hundreds of feet thick andis traditionally subdivided into four major stratigraphic units,or lithofacies, Fig. 2. The uppermost shale unit is made up ofmainly shale, with occasional thin, interbedded reservoirsands. The next unit, the Upper Stringer, consists of interbeddedsands and shales with minor amounts of ironstone, mainlysiderite, and very thin coal layers. Below the Upper Stringerunit is a thick, massive sand unit, which we will also refer tointerchangeably as the Main Sand. The lowermost unit, theLower Stringer, is predominantly shale with minor interbeddedreservoir sands.Most wells drilled to date in the area are vertical and havetargeted the Main Sand unit. Further field development is targetingthe thinner sand reservoirs within the Upper Stringerunit, which are best developed on the flanks of the structure.GEOPHYSICAL APPROACHFig. 1. Offshore Arabian Gulf area of interest.The stringer sands are not visible on the stacked seismic data orafter acoustic impedance inversion, due to the weak P-impedancecontrast between the sands and the surrounding shales.Further attempts to find a post-stack seismic attribute thatwould identify the sands were unsuccessful. The problem wasfurther complicated by the need to delineate the verticallystacked thin sands in 3D space, thereby allowing engineers toplan horizontal wells to target different sand bodies. Synthetic54 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

modeling had shown the S-impedance contrast between thesands and shales to be higher than the P-impedance contrast.With the expectation that the answer on how to delineate thesands would be found in the S-wave component of the seismicdata, the search for a solution was redirected to focus on theshear characteristics of the sand and shale sequences.Geophysics Background TheoryIt has been observed 1 that the variation of reflected amplitudeswith offset is dependent on the difference in Poisson’s ratio values() between the rock layers, leading to the amplitude variationwith offset (AVO) theory to seismic exploration. In physicalterms, Poisson’s ratio is the change in the width of a rock samplerelative to its length in response to an applied stress, Fig. 3. Generally,empirical data show sands to have a lower Poisson’s ratiothan shales. In mathematical terms, Poisson’s ratio can berewritten in terms of S-wave velocity (Vs) and P-wave velocity(Vp), Fig. 4 — two rock properties described by the Zoeppritzequation. For ease of use as a lithology indicator, Poisson’s ratiocan simply be written as the equivalent Vp/Vs ratio and thencalibrated to the different lithologies.Feasibility StudyPetrophysical analysis was carried out to understand the expectedAVO response of the sands. Porosity plotted against P-impedance shows that almost the entire range of porosity valuescorrelates to an impedance range of about 20,000 to32,000 g/cc (ft/s), Fig. 5. The impedance range observed forboth the sands and the shales is the same for all porosity values.This eliminates P-impedance as a measurement to distinguishsands from shales and explains the lack of sand reflectivityseen on the stacked sections. In contrast, cross-plotting theVp/Vs ratio against P-impedance shows a distinct separation inVp/Vs between the sands and shales, Fig. 6. The variation inVp/Vs at the sand-shale interface gives rise to a change in reflectionamplitude with offset at the lithologic boundary 1 .Forward modeling was performed to observe the AVO responseof the sands, Fig. 7. At normal incidence, reflectivity isquite low due to the low impedance contrast between the sandsFig. 3. Poisson’s ratio (σ).Fig. 5. The cross plot of porosity vs. P-impedance demonstrates that both the sandsand the shales are in the same impedance range, explaining why the sand stringerscould not be seen on the stacked seismic data.Fig. 4. Poisson’s ratio can be written in terms of compressional velocity (Vp) andshear velocity (Vs). More simply, the equation can be written as a simple Vp/Vs ratio.Fig. 6. The cross plot of the Vp/Vs vs. P-impedance shows a separation of the sandsand shales. This relationship will later be used to calibrate the seismic results withlithology.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 55

Fig. 8. AVO response of the sand zone of interest. The near angles have a lownegative reflectivity becoming increasingly negative with increasing angle.Fig. 7. AVO modeling of the stringer sand zone of interest. The target is a thick sandencased in shale.and shales. The amplitude at the interface is negative and increaseswith an increasing angle, Fig. 8. Therefore, the changein lithology from shale to sand results in an observable variationof the seismic amplitude with offset.Based on these modeling results, it was decided to invert forVp and Vs using the pre-stack data to generate a Vp/Vs volumethat could distinguish the sands from the shales withinour zone of interest.Data ProcessingThe ocean bottom cable 3D seismic data, acquired in 2003,were reprocessed to preserve the relative amplitudes and reducemultiples and coherent noise in the gathers. The processingsteps included:• Data loading and quality control.• Coupling correction.• Linear noise removal.• Exponential gain.• Geophone/hydrophone phase matching and summation.Fig. 9. The left panel shows an input gather with converted S-waves and linear noise highlighted. The middle panel is a post-NMO gather with demultiple, linear noise filteringand velocity filtering to remove the S-wave energy applied, and with areas of NMO stretch highlighted. The right panel shows the processed gather with a 35° mute applied.56 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

• Shot and receiver domain linear noise removal.• Surface consistent scaling, deconvolution and residualstatics.• Velocity analysis – 1 km x 1 km 4 th order normal moveout (NMO) correction.• Pre-stack time migration.• Velocity analysis – 0.5 km x 0.5 km 4 th order NMOcorrection.• Multiple suppression and linear noise removal.• Residual static alignment.• Angle stack generation.Of particular concern was the presence of noise in the gathers,Fig. 9. The left panel shows that linear noise and converted S-waves dominate the gather. The middle section is the gather afterthe noise removal processing. It shows a slight NMO stretch, butwith the mute restricted to 35° in the right panel, the NMOstretch is largely removed. Angle sub-stacks were generated atseven angle intervals (0° to 35° at 5° intervals) using ray tracing todetermine which offset traces to stack in each sub-stack.Log EditingEleven vertical wells were available for wavelet extraction andgeneration of a low frequency model for the inversion process.Invasion corrections were applied to the density and soniclogs, although the invasion effect was minor. Missing andpoorly recorded sections were reconstructed using regressionanalysis. Only four wells had shear sonic logs, so these wereused to generate pseudo-shear logs for the other wells. As a finalquality check, Vp/Vs curves were generated for each wellto ensure the values were comparable to those obtained usingdirect log measurements.InversionA simultaneous offset inversion approach was used to solve 2for Vp and Vs. Seven sub-stacks, at 5° intervals, were used tosimultaneously invert for the Vp and Vs using the Aki-Richards approximation of the PP reflectivity component ofthe Zoeppritz equation 3 :where, ß and a are the shearand acoustic velocity averages across an interface, and ß and a are the changes in S- and P-wave velocities across the interface.P= Vp ——sin , where Q is the average of incident and transmittedangles across the interface.The initial steps of the simultaneous offset inversion workfloware the same as for a P-impedance inversion. This requiresextraction of wavelets for each angle stack, building a low frequencybackground model, and refinement of the interpretation.The simultaneous offset inversion is then run to solvedirectly for P-impedance, Vp/Vs, and density. The generalinversion workflow is shown in Fig. 10. The detailed workflowconsists of the following steps:• Input the edited petrophysical data followed by a timedepthcalibration.• Build a low frequency solid model using initial horizonsFig. 10. The generalized simultaneous offset inversion workflow used for thisstudy. Individual Vp and Vs volumes are generated and used for producing aVp/Vs ratio volume.and calibrated well logs.• Extract a wavelet using the full offset stack.• Generate a post-stack acoustic impedance inversion volume for interpretation refinement.• Update the horizon interpretation and low frequencymodel, repeat the acoustic impedance inversion and update the interpretation using the results.• Generate angle sub-stacks and extract wavelets for eachangle stack.• Run simultaneous offset inversion and output Vp/Vs,density and P-impedance volumes.The horizons are interpreted one final time using the finalVp/Vs volume to better map the sands. Overall, it has beenobserved that the simultaneous offset inversion consistentlyproduces improved resolution when compared to post-stackacoustic inversion. This is because the extracted anglewavelets are used to invert each angle stack independently,thereby avoiding the distortion of the wavelet caused by summingtraces across all offsets, as in normal stacked sections.Depth ConversionThe objective of the imaging process is to provide engineers witha 3D depth picture of stringer sand distribution to optimize thedrilling of horizontal wells. To generate this depth volume, it isimportant to convert the inversion results from time to depth asaccurately as possible because of the thin nature of these sands.Nine vertical wells located within the 75 km² study area wereused to build the initial velocity model for depth conversion.The velocity model is generated for input to the inversion aspart of the low frequency model building process. Velocitiesfrom the calibrated sonic logs are interpolated using the interpretedhorizons to generate the 3D velocity model. Using thisvelocity model for depth conversion can introduce depth errorsin excess of 20 ft, which is not accurate enough when drillinghorizontal wells targeting thin sands. To reduce these depth er-SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 57

ors, the tops from 23 existing horizontal wells were incorporatedto adjust the initial 3D depth conversion velocity model.The 3D velocity model refinement process begins by snappingthe depth converted horizons, generated using the initialvelocity model, to all tops. At each location where log tops areavailable, a difference is calculated for each depth horizon. Theerrors for each horizon are then interpolated throughout thearea of interest as “error surfaces,” which are subtracted fromthe original depth horizons to generate best-fit depth horizons.These error corrected depth horizons are then converted totime using the same velocity used for the depth conversion.Two sets of interval velocities are calculated: one from the errorcorrected time horizons, and the second using the originaltime horizons with the following simple equation:V int = (D horizon2 – D horizon1 )/(T horizon2 – Thorizon1 ).The ratios of the original interval velocities to the correctedinterval velocities are calculated for each horizon. These ratiosare then applied as scalars to the velocity cube to scale theoriginal 3D velocity model. The final step is to convert theVp/Vs volume to depth with the new 3D velocity volume.Figure 11 demonstrates the importance of accurate depth estimationfor horizontal well planning. The original depth convertedVp/Vs section on the right shows the stringer sand to be about 55ft shallower than the depth section on the left, which was generatedusing the refined velocity model. As evident in Fig. 12, thewell, Well-A, later encountered the stringer sand as predicted inthe depth image converted using the refined velocity model.Drilling ResultsThe pseudo-Poisson’s ratio volume was delivered in time to replanand drill four wells in rapid succession. All original wellplans were altered to maximize sand exposure along the mostefficient trajectory as shown in the Vp/Vs volume. Drilling resultsfrom two of these wells are presented in this article. Thedrill plan for Well-B, Fig. 13, was altered to test two sands atdifferent intervals. The upper stringer sand was found as predictedin both vertical depth and lateral extent. The bit wasthen dropped to target the lower sand. It was expected to drillFig. 11. The original depth converted Vp/Vs volume to the right only incorporateddata from the available vertical wells. The image to the left is the depth volumegenerated using a refined velocity model incorporating the tops from thehorizontal wells. The target sands were almost 55 ft deeper using the refinedvelocity model and this was later confirmed through drilling.Fig. 12. In Well-A, the upper and lower stringer sand units were targeted anddrilled based on the simultaneous offset inversion results. The shale intervalseparating the two was also clearly identified within the limits of seismicresolution. Other large sand packets detected on the left will later be drilled froma closer platform. The vertical scale of the plot is exaggerated.Fig. 13. Well-B encountered sand as predicted and the dip directions of the twosand bodies were determined using a remote resistivity detection tool. Verticalscale is exaggerated.only shale along the way; however, a very thin sand intervalwas encountered, well below seismic resolution. The lowerstringer sand was encountered as expected in vertical depthand was drilled through its extent to total depth (TD) asplanned. In addition to proving the ability to detect the sandsbased on the Vp/Vs volume, we gained the advantage of lowerwell tortuosity, and consequently faster rates of penetrationresulting in a lower risk completion.Well-A was drilled using a remote resistivity detection toolto aid in geosteering the well. This tool enables the detection ofresistivity contrasts across an interface within a radius ofroughly 10 ft to 15 ft around the borehole. The middle stringersand was encountered as expected, and drilling pursued thissand up dip. Drilling into the sand, the remote resistivity tooldetected the channel boundary. Seismic data predicted that thesand’s dip direction would change and this was confirmed bythe resistivity tool while drilling forward through the goodsands. The well then encountered a dirty sand and shale intervalbefore entering the second channel shown on the left of thesection. There is a 10 ft to 15 ft depth error as we move westacross the seismic section, which accounts for the match seenon the gamma ray.For the four wells drilled based on the Vp/Vs volume, thesand exposure per well was comfortably above the minimum58 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

threshold needed for completion. Relative to similar horizontalwells previously drilled in the same area, the four wells were agreat success, with greater than average stringer sand exposure.The rate of production per well has also dramatically increasedand the cumulative oil produced per well is expected toshow a similar increase. In addition, the increased image resolutionof the inverted volumes allowed the faults to be mappedin much finer detail than before, and this helped explain thevariations in the oil-water contact that had been observed.CONCLUSIONSImaging thin sand stringers in the Northern offshore area of SaudiArabia has been a challenge due to the weak acoustic impedancecontrast between the sand and shale sequences. Multiple offsetelastic inversion has provided a solution by simultaneously invertingselected angle sub-stacks and generating a pseudo-Poisson’sratio volume (Vp/Vs) calibrated to gamma ray logs. As a result,thin sands have been successfully imaged and net sand footage ingeosteered wells has appreciably increased. Incorporating the topsfrom all horizontal wells has increased the number of time-depthcontrol points by an order of magnitude, thereby allowing depthconversion errors to be dramatically decreased to meet geosteeringdepth control requirements. Planning wells based on informationthat is more accurate has further resulted in a decrease in thetortuosity of horizontal wells and completions, consequently reducingdrilling risk and associated costs. Lower tortuosity also resultsin a faster rate of penetration and reduced rig time. As a nextstep, the improved understanding of the sand-shale distributionby incorporating seismic inversion data is expected to aid in generatinghigh resolution object-based 3D geocellular models.ACKNOWLEDGMENTSThe authors would like to thank the management of SaudiAramco for their support and permission to publish this article.REFERENCES1. Koefoed, O.: “On the Effect of Poisson’s Ratios of RockStrata on the Reflection Coefficients of Plane Waves,”Geophysical Prospecting, Vol. 3, Issue 4, December 1955,pp. 381-387.2. Pendrel, J., Debeye, H., Pedersen-Tatalovic, R., et al.:“Estimation and Interpretation of P and S ImpedanceVolumes from Simultaneous Inversion of P-wave OffsetSeismic Data,” Society of Exploration GeophysicistsExpanded Abstracts, Vol. 19, 2000, pp. 146-149.3. Aki, K. and Richards, P.G.: Quantitative Seismology, 2 ndEdition, Herndon, Virginia: University Science Books,2002.BIOGRAPHIESSean Rahati is a Geophysical Specialist withthe Saudi Aramco Reservoir CharacterizationDepartment. He has 21 years of experiencein geophysical data acquisition, dataprocessing and seismic interpretation. Priorto coming to Saudi Arabia 6 years ago, hespent his years working in Texas, Louisiana,Venezuela, Argentina and the U.K. At present, Sean is workingwith the Northern Fields Group in Shaybah and Zuluf fields.He received a B.S. degree in Geophysics from the Universityof Houston, Houston, TX, in 1991, and an M.B.A. withhonors from the University of Texas of the Permian Basin,Odessa, TX, in 2001.Hussain M. Al-Otaibi joined Saudi Aramcoin 1984 and is the Manager of the ExplorationTechnical Services Department. Hisbackground includes comprehensive multidisciplinarytechnical work and managementexperience in production geology,drilling operations, reserves, petrophysics,geophysics and reservoir management.Hussain was a Distinguished Lecturer of the Society ofPetroleum Engineers (SPE) International in 2004-2005. As amember of the SPE Oil and Gas Reserves Committee, he participatedin formulating the 2007 SPE/WPC/AAPG/SPEE PetroleumReserves Classification and Definitions. Although Hussainis a past president and one of the main founders of the DhahranGeosciences Society (DGS), and he now serves as President ofthe American Association of Petroleum Geologists (AAPG)Middle East Region (2009-2011).Hussain received his B.S. degree in Petroleum Geology fromKing Fahd University of Petroleum and Minerals (KFUPM),Dhahran, Saudi Arabia.Yousef M. Al-Shobaili is currently theNorthern Onshore Fields Group Leader atthe Reservoir Characterization Department.Since joining Saudi Aramco in 1994, he hasworked in several disciplines within theExploration and Petroleum Engineeringorganizations.Yousef’s experience covers several reservoir aspects,including reservoir evaluation and assessment, reservoirmanagement and engineering assessment, petrophysicalintegration and reserves estimation and assessment, just toname a few. He has also trained several summer students,geologists, geophysicists and reservoir engineers, and hedeveloped an in-house log interpretation and petroleumgeology training course.Yousef has authored and coauthored 18 technical papers inreservoir evaluation, reservoir description, geosteering, rockmechanics, reservoir management, and dynamics and log/corepetrophysics. He is the founder and the first president of theSaudi Petrophysical Society (SPS).He received his B.S. degree in Petroleum Geology andSedimentology from King AbdulAziz University, Jiddah, SaudiArabia.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 59

Generating Seismicly-Derived High-ResolutionRock Properties for Horizontal DrillingOptimization in the Arabian GulfAuthors: Dr. J.A. Vargas-Guzman, William L. Weibel, Idam Mustika and Qadria AnbarABSTRACTThe classic integration of seismically-derived attributes intogeocellular models by collocated cokriging is revisited, leadingto improved geocellular modeling results above the seismicbandwidth between wells. This article shows a practical approachto the challenge of downscaling and the integration ofthe seismic acoustic impedance (seismic AI) attribute by calibratingit to the heterogeneity defined by the log-derivedacoustic impedance (log AI). The approach is a reduction ofthe downscaling method by full cokriging to a simpler stepwisesequential kriging to estimate the required parameters for stochasticsimulation. A downscaled model AI is created by combiningthe low-frequency seismic attribute with a predictedhigh-frequency component before it is integrated into theporosity model using log data. The current tools of preference,collocated cokriging and/or collocated co-simulation, assumeproportionality between the variogram structures for both thesynthetic log AI and the seismic AI. The problem with this assumptionis that the modeled attribute may closely resemblethe original low-resolution data. If the correlation between attributesis significant, then the resulting “downscaled” realizationsby collocated methods look diffuse, so they are unsatisfactoryfor use in high-resolution geocellular models. Thedownscaling approach is redefined in this study by performinganalytical computations and verifications with real reservoirdata. A proper second order downscaling approach for seismicAI must be based on full cokriging and non-collocated co-simulationusing both the logs and seismic. A complete integrationshould also reproduce the higher order geological heterogeneity,which is contained in the high-resolution well logs but notnormally shown in the seismic attributes. The numerical complicationsof cokriging and the lack of robust tools in most existingsoftware have motivated the development of practicalcollocated solutions that can be implemented with less effort.The contribution of this study is that it provides an alternativenon-collocated approach for better representation of the verticalheterogeneity in geocellular models by downscaling theseismic AI prior to integration.INTRODUCTIONOne of the challenges for integrating 3D seismic impedance(seismic AI) datasets into 3D static geocellular models is thelimited vertical resolution of the original seismic data fromwhich the seismic AI is derived. The question of how to amalgamatethe different resolutions between the vertically detailed3D static models, predicted from well logs, and the lower resolutionseismic data has been identified as one of the majorchallenges for integration of seismic data 1 into geocellularmodels. The essence of the problem is that correlations betweenrock properties are scale dependent 2 . Estimating patternsof rock bodies and their properties (e.g., porosity andpermeability) in the high vertical resolution geocellular modelsutilized for the reservoir development is in part limited by thevertical resolution of the input seismic data. Note that seismicdata is usually imported to the modeling software as voxetthick cells similar to pixels in images, and not nodes. The seismicAI contains the low-frequency components of heterogeneity;however, the high-frequency components are unknown.The physical reasons for the resolution limitations of seismicdata, which include the temporal frequency based on the twowaytime sample rate, are described in the literature 3 . To gaininformation about the high-frequency heterogeneity of therocks, one has to resort to the synthetic acoustic impedance(log AI) from the wells. The information in the log AI can beconsidered as the convolution of the low-frequency seismic andthe high frequency impedance signal only available from logs.Therefore, it is natural to conclude that the high-frequencyAI component at inter-well locations of the geocellularmodel should be predicted from the well data before anyfurther integration of the seismic data into porosity modelsusing high-resolution logs is performed. The purpose is togain signal consistency with other logs (i.e., porosity) sampledat high resolution.If a geocellular model is constructed at high resolution (i.e.,from 0.5 to 1 ft average thickness), then direct integration ofthe acoustic impedance seismic volume by collocated cokriging4 may not provide a consistent model because the collocatedcorrelations between the coarse resolution seismic AI andthe porosity well data may not be constant, even within a singlevoxet cell. The reason for this is nonstationarity (i.e., thecovariance is a function of spatial location), and it involves themissing high-frequency components. For example, the welldata may contain stringers of permeable sandstones surroundedby impermeable shale dominating a voxet cell. Seismicacoustic impedance voxet cells have a typical resolution of60 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

a few milliseconds in the time domain, and depending on therock propagation velocity, which is equivalent to a thicknessthat may be 30 times greater than the average cell in a highvertical resolution 3D geocellular reservoir model. The problemcan be quite severe due to the stringer sand bodies, withhigh porosity and high permeability, may be diluted by averagingin thick voxet cells of high acoustic impedance, which maytranslate to false low porosity. The reverse phenomenon mayalso be observed, and low porosity rocks (e.g., low permeabilitybarriers) may disappear after blending into the thick voxetcells of low acoustic impedance.The most popular integration approach, as is proposed inthe literature, is to predict a detailed resolution rock propertyusing collocated cokriging and associated co-simulation tools 5 ;however, as is argued in this study, collocated cokriging shouldnot be used for downscaling (i.e., predicting the high-frequencyAI) at the inter-well locations. Collocated cokriging assumesthe variogram structures for seismic and log data are proportionalfor all lag distances. This is analogous to proposing thatthe power spectra for seismic AI and well log derived AI in thevertical direction should be proportional for every frequency.The Fourier transform provides the relation between spectraand covariances or variograms, i.e., Bochner’s theorem 6 . Themissing high frequencies truncate the power spectrum of theseismic AI; therefore, the variograms (or covariances) cannotbe proportional because the high-frequency component fromthe log AI spectrum will add to the shape of the seismic AIspectrum in the frequency domain. As a result, the high-frequencycomponent of the seismic, which is correlated to itsequivalent high-resolution porosity, needs to be incorporatedinto the AI before the integration of the seismic data in such away that missing components are avoided. This convolution ofthe high-frequency and the low-frequency AI components mustbe done in the spatial domain with geostatistics to achieve theconditioning to the log AI data in the modeling results. Attemptingthe integration in the frequency domain will entail anunconditional stochastic simulation of the high frequency components7 . In addition, a more detailed or higher resolution AImodel requires prior stratigraphic conditioning to the fine resolution,complex spatial geometry of rock bodies in the physicalspace 8 .One data integration approach is to downscale the seismicby incorporating the high-frequency component to match theresolution of the well log data in such a way that spurious correlations(due to resolution differences and non-stationary covariances)are avoided. Another approach is direct simulationbased on parameters constrained by block kriging 9 ; upscalingof the log data instead of downscaling the seismic attributescould help to match the seismic resolution. The weakness ofthis latter approach is that it does not allow the constructionof the desired high-resolution geocellular models by integratingwell log porosity data because the high-frequency log AI informationis lost. An additional uncertainty is that the complexgeometries of the rocks may represent stringer sands or goodreservoir rock bodies that are strongly anisotropic and so lackuniform lateral continuity. Therefore, the prediction of themodel may show a pixel with high porosity at the wellborewhile all surrounding cells do not conform to the expectedgeobody. Some techniques have been devised that use seismicamplitude vs. offset (AVO) analysis to come up with solutionsfor detecting rock bodies using seismic anisotropy of the velocityfield 10 . Such techniques are destined to fail if the limitationsof resolution are severe. A review of the state-of-the-art use ofrock physics and geostatistics is available 11 . It is evident thatthe high-frequency variations in impedance and other seismicproperties cannot be measured in practice; therefore, after revisitingthe theory required for a sound downscaling, thisstudy proposes a simple and practical methodology that relaxesthe hard assumptions imposed in conventional collocatedmethods. The theoretical principles for downscaling correlatedvariables are detailed 12 . Enhancing the vertical resolution ofseismic is not a unique process, as the results are still stochasticpredictions; a sound downscaling strives to avoid unrealisticresults due to spurious correlations during the integration ofthe data.A similar scaling situation is the use of prior low-resolutionnumerical cellular models, of porosity and permeability, whichneed to be locally updated to higher resolution with more detaileddata for single platform flow simulations. Another exampleis the use of gamma ray or density rock property modelsdownscaled for geosteering operations. In this study, these typesof high-resolution models are named sector models and areused to guide the drilling from offshore platforms. The real limitationis not only the limited vertical resolution, but horizontalresolution as well, as both resolutions are not independent ofeach other. History matching is performed in sector models usingboundary flux conditions extracted from the whole reservoirmodel. Therefore, consistency between a sector model andthe prior model is a prerequisite for downscaling. The practicalimportance of downscaling for nonconventional resources wasdiscussed 13 presenting an example from the Athabasca oilFig. 1. Clastic reservoir schematic showing stratigraphy and sand-shale facies(above) and porosity and permeability (High=Red) in a faulted sector model(below). MRC well paths are also shown.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 61

sands. A simple approach is to perform a stochastic downscalingthat yields results that are conditional to the prior coarseresolution model, assuming second order correlations only, andvery abundant local well data. Practical implementation ofcommercially available tools suggests that proper data integrationrequires a detailed workflow to avoid the introduction ofspurious correlations. A sound integration of the seismic toother higher vertical resolution data (e.g., porosity logs) is criticalfor improving the drilling and completion practices formaximum reservoir contact (MRC) multilateral wells, Fig. 1,used in field development.CROSS-CORRELATION: THE FUNDAMENTAL LAW OFDATA INTEGRATIONFig. 2. Nonproportional variogram components for coarse and fine resolutionacoustic impedance.Acoustic impedance in a voxet cell is equivalent to the combinationof impedances of higher resolution elements (i.e., logs)as follows: Ī = 1 – n where v j is the velocity and r j is the density ofeach element. Note that “AI” is noted as “I” for simplicity inthe mathematical formulations. The averaged computation isĪ= - rv - + cov(p,v). The covariance cov(p,v) is the second ordersimilarity between the attributes, and - rand - v are the arithmetic(first order) averages. Therefore, not only the mean valuesneed to be known, but the associated covariance values haveto be included for exact upscaling. In statistical terms, the covarianceis the entire average of all cross-covariances withinthe cell. A concept a bit less popular than the classic Pearsoncorrelation coefficient is the cross-correlation coefficient 14 .Two distinct rock properties can have nonproportionalcovariance functions, or nonproportional variograms, Fig. 2.This is common to all data integration exercises, and the crosscovariancefunction is therefore nonproportional to either oneof the individual covariance or variogram functions. At least intheory, if the synthetic log is correctly scaled to the seismic volumevoxet, the spectra for the same low-frequency componentsof acoustic impedance data should be proportional. Therefore,a lack of good correlation between a synthetic acoustic impedancelog AI and the seismic AI after depth matching is really anissue of resolution.The negative Pearson’s correlation coefficient betweenporosity(∅) and acoustic impedance I is Eq. 1.If you have numerous realizations of a simulated porosityfield ∅(x) and an acoustic impedance field I(x) at high resolutionfollowing the well resolution log AI data resolution, thenit is evident that numerous realizations of the simulated fieldare made of one random variable ∅(x j ) at each cell or location(e.g., point node or volume element). The variable x j representsthe 3D coordinates x of each cell’s “j” center. The numerousrealizations are conditional to the same input data values (i.e.,core porosity well data and unique coarse resolution seismicAI). Therefore, the high-resolution model AI has to yield thecoarser seismic AI after upscaling (i.e., averaging of smallercells should yield the coarser cell data). The correlation coefficientfor two random variables of porosity at locations x i andx j is abbreviated as r(∅ j , ∅ k ). The correlation for the acousticimpedance at those two locations is r(I j , I k ), and the correlationbetween acoustic impedance and porosity is r(I j , ∅ k ). Thislast term is called the cross-correlation and is usually computedfrom an extension of Eqn. 1, using the cross-covarianceinstead of the covariance, and using lag distances to representpairs of variables separated in different cells. Therefore, strictlyspeaking, cross-covariance is just covariance between two attributerandom variables placed at two separated locations, jand k. The cross correlation is related to the cross-covarianceas follows: (1)Since the pair-wise covariance for all pairs of cells cannot beknown as a priori, geostatistics uses a functional covariance estimatedfrom stationary assumptions in the data. Such a covarianceis directly related to variograms. The variogram forthe finer resolution shows the low range of variability, and thevariogram for the coarse resolution shows the long range ofvariability only, Fig. 2. Cross-covariance is also represented byfunctional forms, and the procedures of this type of modelingare described in the literature 14 . The cokriging and sequentialcokriging approaches utilized for downscaling require analyticalmodels of cross-covariance, which are obtained using anambi-rotational technique generalized from Min/Max AutocorrelationFactors (A-MAF), which is a spatial extension of thePrincipal Component Analysis (PCA). A numerical example ofthis approach is available 15 , and it is suitable for multivariatemodels with up to two nested variogram structures.THE COLLOCATED SIMPLIFICATION OF COKRIGINGCokriging is an essential tool to generate co-simulation parameters,and the approach is described in various publications 14,16 . Before the advent of sequential kriging, difficulties in invertinglarge matrices and handling cumbersome unstable systemsof equations, to solve full cokriging systems, pushed geostatisticiansto consider a simplification termed collocated cokriging4 . The result is that collocated cokriging is currently themost widely used approach for data integration, and it is availablein commercial software throughout the industry. A characteristicof collocated cokriging is that the weight numbersused to estimate porosity from acoustic impedance becomeconstant. If the acoustic impedance data is standardized (i.e.,centered with mean zero and variance one), the collocated ap-62 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

proach for standardized data can be written as:(2)where w ϕ j are the sequential kriging weights to update theporosity estimates with data residuals after the acoustic impedancehas been projected to the space of porosity. Note that collocatedcokriging is formulated here as a stepwise approach toavoid a simultaneous solution 7 . The collocation allows the useof the correlation coefficient in the weight, and this correlationcan be made spatially variable. In addition, the seismic AI dataI β is not sparse, and therefore represents constant values withinthick voxet cells. The geostatistical theory shows that the modelis valid if the functional cross-covariance and the individualcovariances are proportional (i.e., intrinsic co-regionalization).Practical applications of collocated methods show that theporosity in the collocation behaves as a projection of theacoustic impedance following a linear regression type ofmodel. The end results show that such projections look“blocky,” or resemble the original coarse resolution data, regardlessof the detailed grid utilized for the geocellular modeling.If the correlation coefficient is very high, the collocationgives a projected copy of the coarse seismic AI data, which iscalled secondary input data in the geostatistical theory of cokriging.This problem was observed during the integration ofacoustic impedance in the construction of high-resolutionmodels to determine platform placement for offshore drillingusing petrophysical properties. The use of a constant correlationmay also lead to spurious local correlations between theprimary well AI data and the secondary seismic AI.Oz and Deutsch (2002) 1 examined the scale dependent correlationtheory that was developed 2 , and concluded that crosscorrelations for properties (e.g., seismic and well log data) arenot independent of the resolution; therefore, the cross correlationcannot be ignored during the integration of data. The artifactsobserved in practice may disappear if the correct nonproportionalcross-covariance and covariance are utilized with fullcokriging estimation of the co-simulation parameters. In thisarticle, analysis and testing suggests that collocated cokrigingshould not be used for spatial estimation of properties that areat different resolutions. The main reason is that properties atdifferent resolutions have nonproportional variograms. If theproperties have proportional variograms, they respond to “intrinsicco-regionalization” in geostatistics 14 .STOCHASTIC SIMULATION AND ESTIMATION MADESIMPLEThe simulation operation requires drawing numbers from aparametric conditional cumulative probability distribution ateach location. The simulation process uses a random numbergenerator to draw property values from a Gaussian distributionN(m j ,s j ) of values. The required parameters are the meanm j , and the standard deviation s j at each location, j. These parametersare usually estimated by kriging. The most efficientapproach for cokriging estimation of a rock property (e.g.,porosity) is called sequential cokriging. Successive or sequentialcokriging uses one data value at a time, and does not reusealready incorporated samples, (not to be confused with krigingwithin the sequential Gaussian simulation). For example, assumeyou have high resolution acoustic impedance at manylocations and porosity at fewer locations. If you take a singleporosity data point ∅ ∝ and one acoustic impedance data pointI β at each step of the estimation process, then, the estimatedporosity is:(3)In the first step, sparse acoustic impedance data I i β is used toestimate porosity at all locations (without using the porositydata). The weight w I,ϕ is used to convert the acoustic impedanceto porosity estimates at sample locations, and the weight,jw I ,ϕ is used to convert acoustic impedance into porosity atnon-sample locations. The partial result is an estimated porositythat does not honor the log data. The weight w ϕ,s is thenjapplied to match the log data using the residual of the porositydata minus the prior estimate, which is ϕ ∝ -w α I,i I β . The weightsmust come from ratios of conditional cross-covariances andconditional variances 17 . The approach was derived usingBayesian partitions of data sets, and it has been demonstratedthat the approach is as numerically exact as full cokriging. Theweights for acoustic impedance in sequential cokriging are notconstant, as used in the collocated version, Eqn. 2. The advantageof sequential kriging is that it avoids the inversion of matricesor the cumbersome solutions of large systems of equationsthat were initially proposed in the matrix form ofcokriging 16 .PRACTICAL DOWNSCALING AND SIGNALEQUALIZATIONThe seismic AI contains the low-frequency components of heterogeneity;however, the high-frequency components are unknown.To gain information about the high frequency heterogeneityof the rocks, one has to resort to the log AI at thewells. The information in the log AI is equivalent to the convolutionof the low-frequency seismic and the high-frequency logcomponent, which is unknown outside the wellbores. Therefore,the high-frequency component should be predicted fromthe well data at the inter-well locations to gain modeling consistencybefore further integration of the seismic data into theporosity models, using the high-resolution logs, is performed.The prediction of the high-frequency component can be madein the spatial domain using the sequential kriging and simulationtools described above.If a point set, extracted from the voxet cell centers, is paintedwith seismic and well log AI data, the observed Pearson correlationin a standard data cross-plot corresponds to the correlationof voxet cell size (blocks), or averages, and the finer resolutionlog AI. Estimation of properties (e.g., hydrocarbon in place)with upscaling requires correct block averaging of porosity asprovided by block cokriging. This is equivalent to estimatingblocks using finer resolution data. Moreover, the downscalingproblem requires the corresponding enhancement of local vari-SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 63

Fig. 3. Seismic acoustic impedance (High=Blue; Low=Red) and the geocellularmodel (left) and the schematic workflow (right) for the carbonate reservoir example.ance to match the short-range variograms and/or high-frequencysignals. Such high-frequency signals do not exist inseismic AI; therefore, any attempt to predict finer resolutionproperties will be unrealistic without the addition of the highfrequencycomponent derived from other data. It is wellknown that block cokriging methods can be used to estimatefiner resolution from coarser blocks (voxet cells) of data; however,one has to keep in mind that the original seismic signalinformation represents “intensity” properties, which belong toa continuous low-frequency signal (not blocky square waves).Therefore, blocking caused by painting seismic into a voxetand the geocellular model is not a strictly continuous representationof the original seismic signals. For instance, if you haveseismic data and one single well to construct a porosity model,then a cokriging estimate of points using a continuously sampledsignal will look geologically more realistic than the blockydata. Since the point set from seismic does not physically representblocks or voxet cells, the sampled points of the seismicAI should be treated as a point support low-frequency “component”of the more finely sampled, variable log AI. Additionalpoints from the well log AI locations, where the voxetcell center is not collocated within a reasonable tolerance,should not be taken directly from the seismic AI as data, andthey should match the original low-frequency vertical seismicsignal. We call this vertical match between the low-frequencyseismic at the well locations and in the geocellular model“equalization.”The physical collocation of seismic and log AI generated usingcollocated cokriging methods will be correct only when theprimary and secondary variables have proportional covariancefunctions. This is equivalent to having proportional spectra inthe frequency domain. Proportional spectra can be achieved bysignal equalization matching the phase and wavelengths observedin the vertically more detailed synthetic data. The seismic AIonly provides realistic information about the low frequencies,as the higher frequencies are aliasing by the low vertical resolution.Therefore, one could sample points that represent thelow-frequency signal from the centers of the voxet cells. Theassumption made here is that the voxet cell center node containsthe average values for each voxet cell. Theoretically, theequalization can be done in the frequency domain by simulatingan unconditional high-frequency component. The model AIhas to be conditional to the well data; therefore, the high-frequencycomponent is estimated and simulated with geostatistics.The methodology for geostatistical simulation of componentsis explained in Vargas-Guzmãn (2003) 7 . For practicalpurposes, an exhaustive sampling of all centers of the voxetcells in depth or time domains may be enough to merge withthe well data. If sequential kriging is utilized, then the higherfrequency data is composed of residuals in the well log AI.The simplified workflow for downscaling, Fig. 3, is as follows:Sample A is made-up of all voxet cell center sparse pointswith seismic AI values and is assumed to be the mean of a randomvariable that can be simulated “within” each voxet cell.Sample B is made-up of synthetic wireline acoustic log AI (I a )data, which is assumed to carry the point support heterogeneitywith second order covariance and/or higher order information.Sample A is equalized to sample B by comparing depths,means and vertical variograms. The sample B data should beable to match sample A’s low frequencies and first order parameters.The residual data that corresponds to the high-frequencybandwidth is then filtered out to construct a model forthe residuals. The simulation of residuals must be done in sucha way that the combination of all the frequencies gives backthe probability distributions observed in the log AI from thewells. A practical approach is to use the cross-plots to shift themeans and decide on a realistic regionalization based on stratigraphy,facies and rock type regions. Sample B has histogramswith mean and variance that are the target of the downscalingprocess for each rock region, strata and/or rock type.Sample A no longer represents the voxet cells. Instead, itrepresents a sample set of sparse nodes or points, I β , which isone point taken at the center of each seismic AI voxet cell. Theassumption here is that the center of mass of each voxet carries,in reality, a data point that coincides with the block average.The successive mathematical formulation to predict the impedance,including the seismic AI and well log AI at each nodej, from surrounding data is:(4)where w αIβ is the sequential cokriging weight used to estimatethe acoustic impedance from sparse samples (nodes) of seismicjAI, and w Iβ is the weight used to estimate the posterior highfrequencyresidual from log AI (using one data point at atime). The end result is a model of AI that is equalized to thelog AI from the wells, and so is better related to any otherhigh-resolution data in the frequency domain. This means thatany new wells drilled should provide log AI data that, first, atleast on average matches the AI and, second, possibly resemblesthe higher order heterogeneity predicted from numerous realizationsof the model AI. The model AI merges the seismic AIto the high-frequency component extracted from the log AI atsurrounding wells utilized for conditioning.64 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

A second order downscaling is performed with stepwise sequentialcokriging of the seismic and log AI and simulation usingcovariances; however, a more advanced approach has beenintroduced 18 that uses cumulants to add the higher order statisticalinformation. Downscaling in practice requires additionalinformation in the geocellular model to guide the spatialestimations and stochastic simulations. The downscaling approachshould also consider nonstationary stratigraphic modelsof rock types and regions constructed using a priori lowfrequencyseismic, analogs and/or categorical data.The final result of combining the low- and high-frequencyseismic and well AI is a downscaled model AI that can betransformed to the frequency domain. It contains all the frequenciesand spectra in the synthetic log AI, and it should yieldthe coarse resolution voxet after back vertical upscaling. Thedownscaled acoustic impedance is unique only at the centers ofeach voxet cell and at the wells. The rest of the domain is simulated,but looks strongly constrained to the seismic because itis using as many samples (centers of voxet cells) as there are inthe seismic volume’s zone of interest.Most software packages contain univariate simulation toolsto perform modeling by using data sets A and B stepwise withoutmajor complications. In addition, the proposed approachconsiders that adding posterior data on top of the smoothprior can still honor the statistics of the posterior conditioningto the data, Eqn. 3. A word of caution: You cannot assume apriori the independence between the seismic AI and the highfrequencyresidual from the log AI. If some amount of correlationremains, you may have to remove the redundancy beforeconstructing the high-frequency component.INTEGRATION OF DATA AFTER SPATIALDOWNSCALINGThe integration of downscaled acoustic impedance to porositylogs is straightforward, provided that correlations are consistent.Such consistency is achieved by spatial downscaling andintegration. The collocated approach may not produce strongartifacts when both variables (e.g., porosity and acoustic impedance)are at the same high resolution and the specified correlationsare locally valid; however, local departures due tononstationary covariance can cause problems and instability.Therefore, the practical recommendation is to resort to co-simulationwith sequential cokriging if non-stationary covariancebecomes a problem. Note that Eqns. 2 and 3 provide the differenceor information lost by not using sequential cokriging toevaluate the probability distribution parameters for simulationof porosity conditional to the downscaled model AI.CARBONATE AND CLASTIC RESERVOIR EXAMPLESWorkflow Followed in the StudiesThe steps used in the downscaling and integration workfloware summarized:1. The seismic AI is sampled at the centers of the voxet cells asa point set. The seismic AI power spectrum in the verticaldirection is compared to a set of validated wells to makesure the low-frequency components are properly equalizedto the voxet size, upscaled synthetic well log AI.2. The low-frequency component (seismic AI) is estimated atall high-resolution cells in the geocellular model followingthe stratigraphic system of coordinates. Due to the largeamount of data in the point set, stochastic simulation wasnot necessary to generate this low-frequency component.3. The high-frequency data is constructed at the wells by subtractingthe seismic AI model (step 2) from the synthetic logAI. The residuals are treated as conditional components 7 .The high-frequency component is simulated in the geocellulargrid constrained to stratigraphy and rock regions.4. The high and low AI models are combined and the resultsare quality controlled.5. The integration step can be handled in various ways:a. If the variograms of porosity and the model AI are nonproportional,cokriging should be used, which requiresthe cross-covariance.b. A faster approach is to use the cross-correlation between themodel AI and the well porosity data. The model AI isprojected in porosity space with the collocated correlationfor each rock type and stratigraphic zone, and the residualbetween the actual well porosity data and the projectedporosity component is simulated using a conditionalcovariance function created from a conditional spectrum.c. If the downscaled seismic AI is highly correlated to theporosity, using collocated co-simulation works well fordata at the same resolution.d. In the examples that follow, the authors decided to avoidthe use of collocated methods and cross-covariance structures;this allowed a full range of automated vendor softwareto be used. The model AI (which contains the highfrequencysimulated component) was re-sampled followingthe initial seismic AI point set, and then projected tothe porosity space using a linear model. The resultingdata was merged with the well log porosity, and a simplestochastic simulation, honoring the statistics of the porositylog conditional to the merged data, was applied usingthe statistical parameters of the log porosity as the cellsize support. The clastic reservoir example includes priorknowledge of the permeability from the coarse resolutionflow simulation models.Carbonate Reservoir ExampleThis carbonate case study is from the middle Jurassic LowerFadhili formation. The target reservoir is strongly affected bydiagenesis on one of the flanks of a structural anticline. Thisreservoir heterogeneity is critical for the optimal placement ofwater injector wells, used to maintain the reservoir pressure. Inaddition, the reservoir rocks follow a complex progradationdepositional sequence. The rocks are permeable grainstones,SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 65

Fig. 4. Coarse initial (left) and higher resolution (right) acoustic impedance modelsfor the carbonate reservoir (High=Blue; Low=Red).passing to wakestones and mudstones towards the south. Theoverall trends of the rocks can be seen in the seismic attributes.The reservoir coarsens upwards to the north and becomes finetextured towards the south. Dolomitization and other diageneticeffects have caused the reservoir to become less permeableon the flanks. The formation facies vary from peloidalskeletal mudstones to packstones. Variography was performedand the direction of anisotropy (i.e., trend) of the carbonateplatform bodies was identified following a general 120° azimuth.The extensive variability of the reservoir rocks and thediagenetic effects required a careful nonstationary model. Theseismic AI data was incorporated to handle this otherwise unpredictableheterogeneity.A seismic AI volume is of fairly low resolution, Fig. 3. Theobjective of the study was to improve the high-resolution spatialmodel distribution of porosity by integrating the negativelycorrelated seismic AI. Downscaling and integration were appliedafter the sampled point set (as previously explained) was combinedwith the synthetic log AI from the wells. Figure 4 showsthe downscaled acoustic impedance. The initial porosity modelwithout the seismic acoustic impedance data is too continuouslaterally, and could not represent the diagenesis effect. Thedownscaling prior to integration methodology allowed for amore realistic porosity model to be generated. A secondarytrend is extracted at the crest of the anticline from the acousticimpedance. This trend cannot be explained by carbonate deposition,but may be due to deterioration of the rock quality atthe flanks of the anticline due to diagenesis, Fig. 5. Figure 6shows examples of the coarse and fine resolution acoustic impedancefollowed by the porosity model from the integrationof data. The results were checked to ensure the data was properlymatching the spatial location and first and second orderparameters. In addition, history matching was performed (notshown here) in which the contribution of the integration ofdata was absolutely necessary.Fig. 6. Coarse (left) and downscaled (center) acoustic impedance models(High=Blue; Low=Red) and a porosity model (right) for the carbonate reservoir(High=Reddish/Yellow).Clastic Reservoir ExampleIn the second example, an offshore clastic reservoir model wasinitially constructed conditional to hundreds of wells. Therocks consist of a thicker main sand zone overlain by sandstonestringers intercalated with shales. Intermediate rockqualities are shaley sands and sandy shales, which are part offining upward sequences in tidal and distributary channels.The main sand is made up of staked fining upward sands.Some coarsening upward features appear as isolated sand bars.The challenge in this reservoir is to develop production in theupper stringer sands, which are less likely to be intercepted byvertical wells. The development strategy is therefore to drill deviatedwells to intersect the sand stringers, then plug back anddrill a horizontal production section along the stringers, withcompletion at MRC.Additional production drilling required the placement ofplatforms selected from the global reservoir model. The initialareal resolution of cells was 125 x 125 m 2 , and the vertical resolutionwas approximately 2 ft on average. The goal was toconstruct a detailed reservoir model with a 50 x 50 m 2 cell size.The model should contain the same heterogeneities as in theprior coarser well only model, including faults and stratigraphy.The initial efforts showed that downscaling using collocatedtechniques was disappointing; the final model showed blockyartifacts (i.e., too similar properties in neighboring cells) conformingto the coarse voxet and ignoring the new higher resolutiongrid. One of the reasons for the artifacts was that thecorrelation between the prior and the posterior models had tobe kept significant (as shown in the data) to assure that theflux boundary conditions from the coarser resolution model,to be applied during dynamic forecast, would still be valid forthe downscaled versions of the model.After the construction of high-resolution grids, the rockproperties were downscaled from the original coarse resolutionFig. 5. Coarse (left) and downscaled (center) acoustic impedance models, and a high resolution (right) porosity models (High=Reddish) for the carbonate reservoir.66 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

model with additional new wells already existing at the platform.The end results for the sector static models, including thenew, successfully completed wells, are shown in Fig. 1. Thestatistics of the variability (i.e., moments) of heterogeneityshow valid results, which are consistent with the second orderexpectances. These models are being used to place new wellsafter the flow simulations provided a positive match and theexpected flow rates without water encroachment.CONCLUSIONSThe methodology for the integration of seismic AI data intoporosity models has been revisited with a proposal to generatea model AI that combines the low-frequency seismic AI with apredicted high-frequency well AI. Since seismic AI contains onlythe low-frequency components (i.e., within the seismic bandwidth),the high-frequency components have been extracted fromthe synthetic well log AI as residuals, or the difference betweenthe seismic AI and the log AI, after careful depth matching. Thelog AI predicted at inter-well locations is equivalent to the convolutionof the seismic AI and the high frequency unknown geologiccomponent. Predictions of the higher frequency AI maynot have significant correlations with the seismic in practice,but they were performed incorporating the known stratigraphy,facies and/or rock regions to create a nonstationary modelAI. The search for a different integration method is motivatedby some of the current drawbacks in traditional downscalingwith collocated cokriging, mainly the spurious correlationsthat may occur between collocated seismic AI and log AI. Thecorrelations may be spurious in areas where the seismic AI valuesare less representative of the true geology due to a low signal-to-noiseratio. Non-stationarity issues also encouragedsearching for better ways to downscale seismic AI. Downscalingwith signal equalization is implemented in the frequency domain,and conditioning to the wells leads to geostatistical estimatesof the high-frequency component in the final spatialmodel. An advantage of using the approach outlined here isthat it does not require solving systems of equations and resolvingthe stability complications found in full cokriging. Therefore,the proposed parameter estimation via sequential cokrigingfor stochastic simulation is a practical tool for downscalingand integration of seismic attributes into geocellular models.ACKNOWLEDGMENTSThe thoughtful review of Saudi Aramco and SPE colleagues isdeeply appreciated. The authors would like to thank SaudiAramco management for granting permission to publish thisarticle.This article was presented at the SPE Reservoir Characterizationand Simulation Conference and Exhibition, Abu Dhabi,U.A.E., October 9-11, 2011.REFERENCES1. Oz, B. and Deutsch, C.V.: “Size Scaling of CrosscorrelationBetween Multiple Variables,” NaturalResources Research, Vol. 11, No. 1, 2002, pp. 1-18.2. Vargas-Guzmán, J.A., Warrick, A.W. and Myers, D.E.:“Multivariate Correlation in the Framework of Supportand Spatial Scales of Variability,” Mathematical Geology,Vol. 31, No. 1, January 1999, pp. 85-103.3. Wang, J. and Dou, Q.: “Integration of 3D SeismicAttributes into Stochastic Reservoir Models Using IterativeVertical Resolution Modeling Methodology,” SPE paper132654, presented at the SPE Western Regional Meeting,Anaheim, California, May 27-29, 2010.4. Xu, W., Tran, T.T., Srivastava, R.M. and Journel, A.G.:“Integrating Seismic Data in Reservoir Modeling: TheCollocated Cokriging Alternative,” SPE paper 24742,presented at the SPE Annual Technical Conference andExhibition, Washington, D.C., October 4-7, 1992.5. Dubrule, O.: Geostatistics for Seismic Data Integration inEarth Models, Tulsa, Oklahoma: Society of ExplorationGeophysicist, 2003.6. Chiles, J.P. and Delfiner, P.: Geostatistics: Modeling SpatialUncertainty, New York: Wiley and Sons Inter-science, 1999.7. Vargas-Guzmán, J.A., 2003. Conditional ComponentRandom Fields, Stochastic Hydrology and Hydraulics,Stochastic Environmental Research and Risk Assessment,Vol. 17, 2003, pp. 260-271.8. González, E.F., Mukerji, T. and Mavko, G.: “SeismicInversion Combining Rock Physics and Multiple PointGeostatistics,” Geophysics, Vol. 73, No. 1, January-February 2008, pp. 11-21.9. Tran, T., Deutsch, C.V. and Xie, Y.: “Direct GeostatisticalSimulation with Multiscale Well, Seismic and ProductionData,” SPE paper 71323, presented at the SPE AnnualTechnical Conference and Exhibition, New Orleans,Louisiana, September 30 - October 3, 2001.10. Close, D., Stirling, S., Cho, D. and Horn, F.: “Tight GasGeophysics: AVO Inversion for ReservoirCharacterization,” CSEG Recorder, May 2010, pp. 29-35.11. Bosch, M., Mukerji, T. and Gonzalez, E.F.: “SeismicInversion for Reservoir Properties Combining StatisticalRock Physics and Geostatistics: A Review,” Geophysics,Vol. 75, No. 5, September - October 2010, pp. 165-176.12. Vargas-Guzmán, J.A., Myers, D.E. and Warrick, A.W.:“Derivatives of Spatial Variances of Growing Windowsand the Variogram,” Mathematical Geology, Vol. 32, No.7, 2000, pp. 851-871.13. Ren, W., Mclennan, J.A., Cunha, L.B. and Deutsch, C.V.:“An Exact Downscaling Methodology in Presence ofHeterogeneity: Application to the Athabasca Oil Sands,”SPE paper 97874, presented at the SPE InternationalThermal Operations and Heavy Oil Symposium. Calgary,Alberta, Canada, November 1-3, 2005.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 67

14. Wackernagel, H.: Multivariate Geostatistics, New York,Springer, 2003, p. 387.15. Vargas-Guzmán, J.A.: “Fast Modeling of Crosscovariancesin the LMC: A Tool for Data Integration,”Stochastic Environmental Research and Risk Assessment,Vol. 18, No. 2, April 2004, pp. 91-99.16. Myers, D.E.: “Matrix Formulation of Cokriging,”Mathematical Geology, Vol. 14, No. 3, 1982, pp. 249-257.17. Vargas-Guzmán, J.A. and Yeh J.: “Sequential Kriging andCokriging: Two Powerful Approaches,” StochasticEnvironmental Research and Risk Assessment, Vol. 13,Issue 6, 1999, pp. 416-435.18. Vargas-Guzmán, J.A.: “Higher-order Spatial Estimationand Stochastic Simulation of Continuous Properties withCumulants and Higher-order non-Gaussian Distributions,”Geostats 2008, VIII International Geostatistics Congress,Santiago, Chile, December 1-5, 2008.BIOGRAPHIESDr. Jose Antonio Vargas-Guzmán joinedSaudi Aramco in 2002 and works as a Consultantwith the Reservoir CharacterizationDepartment, Geological Modeling Division.During his career, he has been involved inmathematical applications to 3D geologicalmodeling and evaluation, and he is the seniorauthor of many journal papers, book reviews and bookchapters; he and has received numerous literature citations. TheInternational Association for Mathematical Geology (IAMG)conferred on him the Best Paper Award from the MathematicalGeology journal for his peer-reviewed paper on successive estimationof spatial conditional distributions in 2003. The IAMGalso bestowed on him the Best Reviewer Award from the Journalof Mathematical Geosciences in 2007.Jose Antonio is a former Fulbright and DAAD Scholar. In1998, he received his Ph.D. degree from the University ofArizona, Tucson, AZ, where he has also served as a researchassociate, instructor and full time faculty member. He wasgranted a graduate scholarship and a post-doctoral fellowshipwith funding provided by the U.S. Nuclear RegulatoryCommission (NRC) and the Department of Energy (DOE),respectively. Also, he was a research fellow in advancedgeostatistics at the University of Queensland, Australia. In the1980s, he served as a Chief Geologist for Société Générale deSurveillance (SGS).Jose Antonio’s most outstanding inventions are 3Dgeological modeling algorithms, such as sequential-kriging,stochastic simulation by successive residuals, conditionaldecompositions, transitive modeling of facies, spatial up-scalingof the lognormal distribution, downscaling methods for seismicdata with derivatives of the variogram, scale effect of principalcomponent analysis, power random fields, and cumulants forhigher-order spatial statistics of complex rock systems andheavy tailed distributions of permeability fields.William L. (Bill) Weibel is a Geophysicistwith more than 30 years of oil industryexperience, the last 10 years beingwith the Reservoir CharacterizationDepartment (RCD) of Saudi Aramco’sExploration organization. Since joiningSaudi Aramco in 2000, he has contributedseismic interpretations that have defined the structureand provided reservoir quality estimation of severalfields, including Berri, Qatif, Abu Sa’fah, Dammam, Khursaniyah,Hawtah, Manifa and Shaybah.He has authored and coauthored two technical papersfor conferences held by the European Association ofGeoscientists & Engineers (EAGE) and the Society ofPetroleum Engineers (SPE).In 1981, Bill received his M.S. degree from theUniversity of Arizona, Tucson, AZ.Idam Mustika joined Saudi Aramco in2008 as a Geologist Geomodeler in theReservoir Characterization Department,Geological Modeling Division, and hasbeen a geological modeler at the EventSolution also. He has modeled numerousgigantic reservoirs and worked invarious seismic integration projects, improving models forhistory matching. Before joining Saudi Aramco, Idamworked for Schlumberger, YPF, Maxus, Repsol CNOOC SESand Petronas Carigali Sdn Bhd as a Geomodeler and DevelopmentGeologist, residing in various countries.In 2000, Idam received his B.S. degree in Geology fromPadjadjaran University, Bandung, Indonesia.Qadria Al-Anbar is a Geological Consultantworking with the GeologicalModeling Division. Since joining SaudiAramco in June 1980, she has workedin several different divisions, includingthe Exploration Division, ReservoirGeology Division, Hydrology Division,Biostratigraphy Division and Northern Reservoir GeologyDivision. Qadria is the second female Geologist in SaudiAramco and the first one to work in building 3D geologicalmodels. Her work has had a big impact on developmentplans for oil fields and the increase of natural reserves.In 2002, Qadria received the 1st Annual TechnicalAchievement Award as a member of the GeologicalModeling Group for her major role in facilitating theconsiderable reserves increase.She received her B.S. degree in Geology from DamascusUniversity, Damascus, Syria.68 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Power of Inventions to Fuel CorporateTransformationAuthor: Dr. M. Rashid Khan, Intellectual Assets Management GroupCenturies ago, President Abraham Lincoln declared that “Thepatent system fuels the fire of genius.” For decades following,the patent system has been used as a business tool to transformcompanies and nations. Cutting edge inventions provide acompetitive edge to businesses, and companies live, die or aresustained by the power of patents.For future corporate impact and transformation, it is importantto ensure that inventions are well aligned with corporatestrategies. The anticipation is that organizations will articulatetheir broad strategies into specific innovative actions, which inturn will lead to novel solutions. For Saudi Aramco, the valueof inventions can go beyond sustaining Saudi Aramco’s role asa reliable supplier of oil and gas, and includes many of the followingbenefits:• The potential “freedom to operate.”• Maximized profitability, through cost savings/revenuegeneration.• Leverage in relationship with external parties, as appropriate.• Long-term diversification (e.g., chemicals or petrochemicals).• Enhanced in-Kingdom research and technology.• Development of a future world-class workforce and aculture of creativity.Each of these would ultimately enhance the development ofSaudi Arabia’s economy.The Intellectual Assets Management (IAM) group of the TechnologyManagement Division is dedicated as the primary resourcewithin Saudi Aramco for the identification, protection and use ofcorporate intellectual property (IP). Corporate gudlines obligateIAM to provide IP services to the Saudi Aramco corporate community.IAM is the primary point of contact for all departmentsexperiencing any IP protection related issues. The many servicesprovided by IAM include: (1) Conduct IP awareness and educationactivities, (2) Assist inventors in developing suitable disclosuredocuments for processing by the Law Department, (3) Assist in thedevelopment of suitable nondisclosure agreements used to protectthe company’s confidential information while enabling the effectivesharing of information for the benefit of Saudi Aramco, (4)Provide IP landscaping and intelligence services, on request, toidentify patent and technology landscapes and to help identify potentialareas for breakthrough developments, (5) Offer day-to-dayservices to the inventors to create value for the company, etc.Patents are one form of IP; other forms include copyrights,trademarks and trade secrets. Not all innovations, however, are inventions.Patents can be used to protect inventions as long as theymeet certain strict legal criteria, the most important of which isthat the invention must be new and non-obvious. Only if thatthreshold is met will a governmental agency grant a patent so thatthe owner can enjoy a period of exclusive ownership, up to 20 years.For a concept to translate into a patentable invention, it is importantthat the disclosure document be completed with sufficientdetails, such that:a. It is enabling with the best mode described.b. Other needed information (no prior publications, any involvement of third parties, inventor personal data, information to identify ownership, etc.) is provided.c. The inventors and the proponent organization appreciatethat there is a continuing obligation to provide input andassistance as required. Confidentiality and employee workproduct privilege cannot be breached.With quality and complete disclosures, IAM can process thesematters efficiently with Law, saving invaluable resources for allparties. The time to file is drastically reduced and the number ofnew patent disclosures increases significantly with quality disclosures.IAM is dedicated to create value for the company by continuingto provide invaluable services.Dramatic New Change in U.S. Patent LawThe America Invents Act has been widely described as thebiggest reform of American patent law in some 50 years.The law will switch U.S. rights to the patent from the present“first-to-invent” system to a “first-to-file” system forpatent applications filed on or after March 16, 2013. Thisshould have a significant impact in the manner businessesmanage their IP.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 69

SAUDI SAUDI ARAMCO’S U.S. U.S. PATENTS PATENTS GRANTED – 2010 – 2010 Summary SYSTEM, PROGRAM PRODUCT AND RELATED(Continued from the Saudi Aramco Journal of Technology, Winter METHODS FOR ESTIMATING AND MANAGING(Continued from the Saudi Aramco Journal of Technology Winter This invention relates to contract procurement and managementthrough an online website over a computer net-2010 issue)CRUDE GRAVITY IN FLOW LINES IN REAL-TIME2010 issue)work. Granted Patent: U.S. 7,860,669, Grant Date: December 28, 2010Mohammed N. Al-KhamisWELLHEAD FLOW LINE PROTECTION ANDWELLHEAD TESTING FLOW SYSTEM LINE WITH PROTECTION ESP SPEED AND CONTROLLER SYSTEM, Summary PROGRAM PRODUCT AND RELATEDTESTING AND SYSTEM EMERGENCY WITH ISOLATION ESP SPEED VALVE CONTROLLER METHODS FOR ESTIMATING AND MANAGINGAND EMERGENCY ISOLATION VALVECRUDEThisGRAVITYinvention relatesIN FLOWto managingLINES INtheREAL-TIMEcharacteristics of aGranted Patent: U.S. 7,823,640, Grant Date: November 2, 2010fluid, specifically to systems, apparatus, program andGrantedPatrickPatent:S. FlandersU.S. 7,823,640, Grant Date: November 2, 2010 Granted Patent: U.S. 7,860,669, Grant Date: December 28, 2010product. The invention provides methods to estimate andPatrick S. FlandersMohammed N. Al-KhamisSummarymanage the flow characteristics of a fluid stream flowingSummarySummary through a pipeline.This invention provides a protection system for a wellheadThis piping invention flow discloses line. The a system protection pressurized system for by a wellheadelectric piping submersible flow line that pump is pressurized (ESP) to protect by a downhole downstream, fluid, and specifically to systems, apparatus, program anddownhole This invention relates to managing the characteristics of aSAUDI ARAMCO’S U.S. PATENTS GRANTED – 2011electric low-pressure submersible rated pump transportation (ESP) to protect and downstreamdistribution product. PROCESS The invention FOR REMOVAL provides methods OF NITROGEN to estimate AND andlow-pressure pipelines, rated and it transportation also provides and a fully distribution automated safety manage POLY-NUCLEAR flow characteristics AROMATICS of a fluid FROM stream flowing FCCpipelines, and fault and test that function. also provides a fully-automated safety through FEEDSTOCKS a pipeline.and fault test function.Granted Patent: U.S. 7,867,381, Grant Date: January 11, 2011COMPOSITION AND PROCESS FOR THE REMOVAL Omer KoseogluCOMPOSTION OF SULFUR AND FROM PROCESS MIDDLE FOR DISTILLATE THE REMOVAL FUELS SAUDI ARAMCO’S U.S. PATENTS GRANTED – 2011SummaryOF SULFUR Granted Patent: FROM U.S. MIDDLE 7,842,181, DISTILLATE Grant Date: November FUELS 30, 2010GrantedKi-HyoukPatent:ChoiU.S. 7,842,181, Grant Date: November 30, 2010 PROCESS This patent FOR relates REMOVAL to the treatment OF NITROGEN of feedstocks ANDto improveKi-Hyouk ChoiPOLY-NUCLEAR the efficiency of AROMATICS operations involving FROM the FCCeffluent productSummarySummaryFEEDSTOCKS streams of hydrocracking or fluid catalytic cracking units.This invention provides a composition and process for removinginvention sulfur provides from a middle composition distillate and petroleum process for hydrocar-re-Omer DISTRIBUTED Koseoglu AND ADAPTIVE SMART LOGICGranted Patent: U.S. 7,867,381, Grant Date: January 11, 2011Thismoving bon sulfur fuels that from exhibit middle high distillate selectivity petroleum at ambient hydrocarbonfuels ture and that high exhibits volumetric high selectivity adsorption at ambient capacity tempera-upon Summary RELIABLE SAFETY SYSTEM SHUTDOWNtempera-WITH MULTI-COMMUNICATION APPARATUS FORture thermal and high treatment. volumetric adsorption capacity uponThis Granted patent relates Patent: to U.S. the 7,869,889, treatment Grant of feedstocks Date: January to improvePatrick the efficiency Flanders and of operation Abdelghani or Daraiseh effluent product11, 2011thermal treatment.GEOSTATISTICAL ANALYSIS AND CLASSIFICATIONstreamsSummaryof hydrocracking or fluid catalytic cracking units.GEOSTATISTICAL OF CORE DATA ANALYSIS AND CLASSIFICATIONOF CORE Granted DATA Patent: U.S. 7,853,045, Grant Date: December 14, 2010 DISTRIBUTED This patent AND relates ADAPTIVE to instrumented SMART safety LOGIC systems forGrantedMustafaPatent:Touati,U.S. 7,853,045,Shameem SiddiquiGrant Date:and TahaDecemberM. Okasha14, 2010 WITH monitoring MULTI-COMMUNICATION and controlling chemical APPARATUS and other field FORMustafa Touati, Shameem Siddiqui and Taha M. OkashaRELIABLE devices SAFETY that are responsive SYSTEM SHUTDOWNto signals for the emergencySummaryshutdown of a process. The patent improves the reliabilitySummaryGranted Patent: U.S. 7,869,889, Grant Date: January 11, 2011This invention relates to the analysis and storage in a Patrick of Flanders communications and Abdelghani within Daraiseh an emergency shutdown system,reduces unwanted trips and adapts processes to aThis database invention of relates the characteristics to the analysis of and geological storage core in a and coredatabase plug of samples, the characteristics and their retrieval of geological based upon core and the correlationsamples of the that characteristics are retrievable of based the data. upon the correla-core Summary safe mode in dynamic conditionsplugThis patent relates to safety instrumented systems fortion of the characteristics of the data.monitoring,SYSTEM,controllingMETHODchemicalAND PROGRAMand other fieldPRODUCTdevicesSYSTEM, PROGRAM PRODUCT AND METHODSthatFORare responsiveTARGETINGto signalsANDforOPTIMALthe emergencyDRIVINGshutdownFORCESYSTEM, FOR MANAGING PROGRAM PRODUCT CONTRACT AND PROCUREMENTMETHODS of a process.DISTRIBUTIONThe patentINimprovesENERGYtheRECOVERYreliability ofSYSTEMScommunicationsGranted Patent: within U.S. an emergency 7,873,443, Grant shutdown Date: January system, 18, re-2011GrantedFOR Granted MANAGING Patent: U.S. CONTRACT 7,853,472, Grant PROCUREMENTDate: December 14, 2010HishamPatent:A. Abdulqader,U.S. 7,853,472,AmmarGrantI.Date:MubarakDecemberand Udai14,M.2010Mulla duces Mahmoud unwanted Noureldin trips, and and adapts Ahmed process Aseeri conditions to aHisham A. Abdulqader, Ammar I. Mubarak and Udai M. MullaSummarysafe modeSummaryin dynamic conditionsThis invention relates to contract procurement and manage-SYSTEMment through an online website over a computer network. FOR program TARGETING to optimize AND energy OPTIMAL recovery DRIVING of a process FORCEor aThis patent METHOD relates AND to a system, PROGRAM methods PRODUCT and user-friendlyDISTRIBUTION IN ENERGY RECOVERY SYSTEMS70 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

cluster of processes under all possible process changes andusing stream-specific minimum temperature approachvalues.METHOD AND APPARATUS FOR SIMULATINGELECTRICAL CHARACTERISTICS OF A COATEDSEGMENT OF A PIPELINEGranted Patent: U.S. 7,876,110, Grant Date: January 25, 2011Jaime P. Perez and Scott D. MillerSummaryThis patent relates to methods and an apparatus for avertingcorrosion of pipelines, and optimizing the detectionand location of defects in coatings on the pipe structureswithout excavation or local physical contact with the pipe.METHOD AND APPARATUS FOR ESTIMATING THECONDITION OF A COATING ON AN UNDER-GROUND PIPELINEGranted Patent: U.S. 7,880,484, Grant Date: February 1, 2011Jaime Perez and Scott MillerSummaryThis patent relates to optimizing the detection and locationof defects in coatings on pipe structures without theneed of excavation or local physical contact with the pipe.OPTICAL METHOD FOR DETERMINATION OF THETOTAL SUSPENDED SOLIDS IN JET FUELGranted Patent: U.S. 7,889,337, Grant Date: February 15, 2011Said S. Al-Jaroudi, Rashed Hadi and Amer Al-ShahriSummaryThis patent relates to a method and apparatus for determiningthe solids content in a hydrocarbon liquid. The inventionalso relates to an optical method for determiningthe total suspended solids content in a hydrocarbon liquid,such as aviation fuel.GAS EXPANSION TRUNK FOR MARINE VESSELSGranted Patent: U.S. 7,905,191, Grant Date: March 15, 2011Thomas J. Scott and Ian HallSummaryThis patent relates to the construction of marine vesselsthat carry liquid cargos, such as very large crude oilcarriers (VLCCs), and specifically to the requirments forproviding cargo expansion space during transit.METHOD FOR WELLHEAD HIGH INTEGRITYPROTECTION SYSTEMGranted Patent: U.S. 7,905,251, Grant Date: March 15, 2011Patrick S. FlandersSummaryThis patent relates to a method and an apparatus for theoperation and testing of a high integrity protection system(HIPS) connected to a wellhead pipeline system.REACTIVE EXTRACTION OF SULFUR COMPOUNDSFROM HYDROCARBON STREAMSGranted Patent: U.S. 7,914,669, Grant Date: March 29, 2011Farhan M. Al-Shahrani and Gary MartinieSummaryThis patent relates to a process for reducing of sulfur compoundsin various hydrocarbon streams, and more specificallyto a liquid-liquid extraction of a hydrocarbon liquidphase within an aqueous phase.METHOD FOR FABRICATING PIPELINE COATINGSAMPLES CONTAINING SYNTHETIC DISBONDSFOR ESTIMATING THE CONDITION OF THECOATING OF AN UNDERGROUND PIPELINEGranted Patent: U.S. 7,924,032, Grant Date: April 12, 2011Jaime PerezSummaryThis patent relates to methods and an apparatus for avertingcorrosion of pipelines by optimizing the detection andlocation of defects in coatings on pipe structures without theneed of excavation or local physical contact with the pipe.CATALYTIC NAPTHA REFORMING PROCESSGranted Patent: U.S. 7,927,556, Grant Date: April 19, 2011Emigdio J. SalmonSummaryThis patent relates to a method and apparatus to take advantageof thermodynamic and chemical equilibrium parametersto increase the efficiency of the naptha reformingprocesses so as to produce larger quantities of octane enhancingcomponents and reduce the amount of gasformed, thereby lowering operating cost.SAUDI ARAMCO JOURNAL OF TECHNOLOGY WINTER 2011 71

METHOD OF DETERMINING SATURATIONS IN ARESERVOIRGranted Patent: U.S. 7,937,222, Grant Date: May 3, 2011Alberto F. MarsalaSummaryThis patent relates to methods for determining water saturationswithin a subterranean reservoir, based on theevaluation of resistivity surveys of such reservoirs.METHOD AND APPARATUS FOR ESTIMATING THECONDITION OF A COATING ON AN UNDER-GROUND PIPELINEGranted Patent: U.S. 7,940,062, Grant Date: May 10, 2011Jaime P. Perez and Scott D. MillerSummaryThis patent relates generally to methods and an apparatusfor averting corrosion of pipelines. More specifically, thepresent invention relates to optimizing the detection andlocation of defects in coatings on the pipe structures withoutthe necessity of excavation or local physical contactwith the pipe.ULTRAVIOLET RADIATION STABILITY ANDSERVICE LIFE OF WOVEN FILMS OFPOLYPROPYLENE (PP) TAPES FOR THEPRODUCTION OF JUMBO BAGSGranted Patent: U.S. 7,947,768, Grant Date: May 24, 2011Milind Vaidya and Ahmad BahamdanSummaryThis patent relates generally to a polyolefin resin and articlesprepared from the polyolefin resin. More specifically, the inventionrelates to a polypropylene resin exhibiting improvedultraviolet (UV) radiation stability and articles preparedtherefrom.SULFUR LOADING APPARATUSGranted Patent: U.S. 7,958,913, Grant Date: June 14, 2011Mohammed S. Al-AwadhSummaryThis patent relates to an apparatus for transferring achemical substance to a movable tanker.SUSPENDED MEDIA GRANULAR ACTIVATEDCARBON MEMBRANE BIOLOGICAL REACTORSYSTEM AND PROCESSGranted Patent: U.S. 7,972,512, Grant Date: July 5, 2011William G. ConnerSummaryThis patent relates to industrial wastewater treatment systemsand methods, and more particularly to industrialwastewater treatment systems and methods using membranebiological reactors.DATA ANALYSIS SYSTEM FOR DETERMINING THECOATING CONDITONS OF A BURIED PIPELINEGranted Patent: U.S. 8,000,936, Grant Date: August 16, 2011Thomas J. DavisSummaryThis patent describes methods and an apparatus to optimizethe detection and location of defects in coatings on pipestructures without excavation or local physical contact withthe pipe.UPGRADING CRUDE OIL USING ELECTRO-CHEMICALLY GENERATED HYDROGENGranted Patent: U.S. 8,002,969, Grant Date: August 23, 2011Esam Z. HamadSummaryThis patent relates to a process and apparatus forhydrogenolyzing crude oil in an electrochemical cell orreactor using hydrogen produced in-situ by the cell.OIL-BASED THERMO-NEUTRAL REFORMING WITHA MULTICOMPONENT CATALYSTGranted Patent: U.S. 8,008,226, Grant Date: August 30, 2011Bashir O. Dabbousi, Fahad I. Al-Muhaish, Tomoyuki Inui,Shakeel Ahmed and Mohammed A. SiddiquiSummaryThis patent relates to a thermo-neutral process for thereforming of petroleum-based liquid hydrocarbon fuels,and more specifically, to the use of a multicomponentcatalyst in said thermo-neutral reforming process. Catalystsutilized in the processes of the invention can includea transition metal containing compound, the metal beingselected from Group V, Group VI and Group VIII of thePeriodic Table, and mixtures of these metals.72 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

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GUIDELINES FOR SUBMITTING AN ARTICLETO THE SAUDI ARAMCO JOURNAL OF TECHNOLOGYThese guidelines are designed to simplify and help standardizesubmissions. They need not be followed rigorously. If youhave additional questions, please feel free to contact us atPublic Relations. Our address, fax and phone numbers arelisted on page 73.LengthVaries, but an average of 2,500-3,500 words, plusillustrations/photos and captions. Maximum length shouldbe 5,000 words. Articles in excess will be shortened.What to sendSend text in Microsoft Word 6.0/95 or higher (do notsubmit UNIX files) via e-mail or on disc, plus one hard copy.Send illustrations/photos and captions separately butconcurrently, both as e-mail or as hard copy (moreinformation follows under Format).ProcedureNotification of acceptance is usually within three weeks. Thearticle will be edited for style and clarity and returned to theauthor for review. All articles are subject to the company’snormal review. No paper can be published without a signatureat the manager level or above.FormatNo single article need include all of the following parts. Thetype of article and subject covered will determine which partsto include.Working titleAbstractUsually 100-150 words to summarize the main points.IntroductionDifferent from the abstract in that it “sets the stage” for thecontent of the article, rather than telling the reader what itis about.Main bodyMay incorporate subtitles, artwork, photos, etc.Conclusion/summaryAssessment of results or restatement of points in introduction.Endnotes/references/bibliographyUse only when essential. Use author/date citation method inthe main body. Numbered footnotes or endnotes will beconverted. Include complete publication information.Standard is The Associated Press Stylebook, 39 th ed.AcknowledgmentsUse to thank those who helped make the article possible.Illustrations/tables/photos and explanatory textSubmit these separately. Do not place in the text. Positioningin the text may be indicated with placeholders. Initialsubmission may include copies of originals; however,publication will require the originals. When possible, submitboth electronic versions, printouts and/or slides. Color ispreferable.File formatsIllustration files with .EPS extensions work best. Otheracceptable extensions are .TIFF, .JPEG and .PICT.Permission(s) to reprint, if appropriatePreviously published articles are acceptable but can bepublished only with written permission from the copyrightholder.Author(s)/contributor(s)Please include a brief biographical statement.Submission/Acceptance ProceduresPapers are submitted on a competitive basis and are evaluatedby an editorial review board comprised of various departmentmanagers and subject matter experts. Following initialselection, authors whose papers have been accepted forpublication will be notified by e-mail.Papers submitted for a particular issue but not accepted forthat issue will be carried forward as submissions forsubsequent issues, unless the author specifically requests inwriting that there be no further consideration. Paperspreviously published or presented may be submitted.Submit articles to:EditorThe Saudi Aramco Journal of TechnologyRoom 2240 East Administration BuildingDhahran 31311, Saudi ArabiaTel: +966/3-873-5803Fax: +966/3-873-6478E-mail: william.bradshaw.1@aramco.com.saSubmission deadlinesIssue Abstract submission deadline Release dateSummer 2012 February 29, 2012 June 30, 2012Fall 2012 May 30, 2012 September 30, 2012Winter 2012 August 29, 2012 December 31, 2012Spring 2013 December 1, 2012 March 31, 201374 WINTER 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Additional Content Available Online at: www.saudiaramco.com/jotComprehensive Parametric Study of Optimal Well Configurations for Improved Productivity – Examplesfrom Saudi Arabian Gas ReservoirsDr. Zillur Rahim, Adnan A. Al-Kanaan, Dr. Hamoud A. Al-Anazi and Ahmed Al-OmairABSTRACTProper quantification of expected well deliverability is one of the most important pieces of information needed for fielddevelopment. Well potential and deliverability depend not only upon reservoir quality, fluid pressure and hydrocarbon characteristics,but also on wellbore trajectory, type and configuration, and hydraulic fracturing. For optimal reserves recovery, somefields may only need vertical wells completed with cemented casing and selective perforated intervals. In some other fields,horizontal and multilateral wells may provide the optimal recovery.Coupled Facility and Reservoir Simulations to Optimize Strategies for a Mature FieldDr. M. Ehtesham Hayder, Satya A. Putra and Ahmad T. Al-Shammari,ABSTRACTIt is important to employ a good production injection strategy to optimize hydrocarbon recovery from a field. Reservoirconstraints on the surface facility change as the field matures. It may be economical to revise the surface facility configurations,rather than retaining the initial design of the surface facility, to maintain the target production level of the field as reservoirconditions change. If reservoir pressure is not sufficient to maintain natural flow, there may be a need for artificial liftmechanisms to keep the well flowing.Revisiting Dielectric Logging in Saudi Arabia: Recent Experiences and Applications in Development and Exploration WellsDr. Denis P. Schmitt, Ahmed A. Al-Harbi, Pablo Saldungaray, Dr. Ridvan Akkurt and Tianhua ZhangABSTRACTDielectric logging was introduced in the late 1970s mostly to measure water filled porosity in the flushed zone independent ofwater salinity and Archie exponents m and n. Although the technology generated a lot of interest upon its introduction, iteventually disappeared over the years, mostly due to the only moderate accuracy of the early devices, oversimplifiedinterpretation models and other hardware related complications.Strategy for the Rapid Transformation of Saudi Arabia by Leveraging Intellectual Capital and KnowledgeManagementSami A. Khursani, Omar S. Bazuhair and Dr. M. Rashid KhanABSTRACTWith technological evolution, intellectual capital (IC) and assets — including innovations, inventions, and the correspondingknowledge that resides within the people of an organization — are increasingly being recognized as a rich avenue for current andfuture development. Current world commerce is characterized by a swing from the old concept of predictability to a state of rapid,radical and discontinuous change, which affects both large and small businesses. Consequently, a competitive enterprise must beincreasingly flexible and agile, and strategies are needed that are responsive to changes as they occur.