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www.csiro.auA deployment strategy for effective geophysicalremote sensing of CO 2 sequestration:Final reportDavid Annetts, Juerg Hauser, James Gunning, Boris Gurevich, Andrej Bona, RomanPevzner, Brett Harris, Milovan Urosevic, Mamdoh al Ajami, John CantEP12519723 October, 2012ANLEC R&DJames Underschultz

Enquiries should be addressed toDavid AnnettsCSIRO Earth Science and Resource EngineeringAustralian Resources Research Centre26 Dick Perry Avenue, KENSINGTON 6151, AustraliaTelephone : +61 8 6436 8517Fax : +61 8 6436 8555email: David.Annetts@csiro.auRecommended citationAnnetts, D., Hauser, J., Gunning, J., Gurevich, B., Bona, A., Pevzner, R., Harris, B., Urosevic, M.,al Ajami, M., Cant, J. 2012, A deployment strategy for effective geophysical remote sensing of CO 2sequestration, CSIRO Report EP125197.AcknowledgementThe authors wish to acknowledge financial assistance provided through Australian National Low EmissionsCoal Research and Development (ANLEC R&D). ANLEC R&D is supported by Australian CoalAssociation Low Emissions Technology Limited and the Australian Government through the Clean EnergyInitiative.Copyright and disclaimer© 2012 CSIRO To the extent permitted by law, all rights are reserved and no part of this publicationcovered by copyright may be reproduced or copied in any form or by any means except with the writtenpermission of CSIRO.Important disclaimerCSIRO advises that the information contained in this publication comprises general statements based onscientific research. The reader is advised and needs to be aware that such information may be incompleteor unable to be used in any specific situation. No reliance or actions must therefore be made on thatinformation without seeking prior expert professional, scientific and technical advice. To the extentpermitted by law, CSIRO (including its employees and consultants) excludes all liability to any personfor any consequences, including but not limited to all losses, damages, costs, expenses and any othercompensation, arising directly or indirectly from using this publication (in part or in whole) and anyinformation or material contained in it.

Response to referee’s commentsThis is the final reviewed version of our manuscript. Referee’s suggestions to change the names, e.g. ofwells, geological formations etc. to reflect current nomenclature, were implemented as a matter of course.Typographical errors were also corrected as a matter of course.Some reviewers commented on the report’s apparent bias in favour of the reflection seismic method.Slightly over 50% of the technical material is concerned to some extent, with the reflection seismicmethod in various survey configurations. Much of the remainder of the technical content is concernedwith the electromagnetic (EM) prospecting method. This report was designed to investigate geophysicalremote sensing of CO 2 . Apart from the reflection seismic method, we are not aware of a technique thatachieves high resolution at the depths required for CO 2 sequestration which are typically deeper than800 m. Structure mapped using reflection seismics can be used to constrain the bounds of CO 2 distributionswhich are derived from methods such as EM (Section 4.2) and gravity (Section 4.3). The reason forthis lies in the physical principles which are exploited by these last two methods. Essentially, geophysicalprospecting methods such as EM and gravity have an inherent ambiguity. In the case of EM, we canresolve the product of resistivity and thickness, and in the case of gravity, a particular response can beproduced by different combinations of density, size and depth. In both cases, knowledge of the structure(provided by the reflection seismic method) significantly reduces the ambiguity of both methods, allowingderivation of resistivity from EM data and density from gravity data, both of which are related to CO 2sequestration. When the reflection seismic method is used in this way, it is important to understand limitson resolution. For this reason, significant effort was spent understanding the uncertainties in structureand CO 2 saturation that could be derived from seismic data (Section 4.1.3).Other reviewers commented on the use of horizontal wells as a survey geometry for monitoring thespatial extent of a CO 2 plume. To a certain extent, their effectiveness will be site-specific, though wemake some general comments. For the reflection seismic method, we expect similar improvements insignal/noise ratio to when ocean-bottom cable (OBC) seismometers are used in marine seismic surveys.Advantages of using OBC seismometers are seen in Section 4.1.4.3. For electromagnetic surveys studiedin Section 4.2, the use of horizontal wells is somewhat more complicated. Intuitively, we expect animprovement in signal/noise ratio because receivers are closer to the change that is being measured, andsimilar improvements to OBC seismic surveys are expected. However, this does not consider the EMsource. If this is placed in a well, then we expect reduced signal-noise ratios because of the generalreduction in source moment (the product of transmitter loop turns, loop area and current) when e.g. alarge loop in miniaturised so that it fits in a borehole. There may be value in using large-moment surfacetransmitters in combination with receivers in horizontal wells, but this is also site-dependent.Some reviewers focussed on what was required from a monitoring and verification program. At thetime of writing, different regulatory regimes applied to on-shore and off-shore sequestration projects.Off-shore projects are covered by Federal regulations while on-shore projects are covered by state regulations.In this report, we based M&V on Federal guidelines, which may differ from a particular State’srequirements. Not all states have yet developed legislation; CCS regulatory frameworks are being developedon a nationally-consistent basis under guiding principles endorsed in 2005 by all Australianjurisdictions. Key to M&V requirements for a particular project is the site plan which is required to iden-| i

Executive summaryThis report describes ANLEC R&D project 3-0510-0030 which is entitled “A deployment strategy foreffective geophysical remote sensing of CO 2 sequestration”.The objectives of this project were to:-• To develop conceptual reservoir models which span the likely geometries and performance of thepotential demonstration flagships;• To forward model possible physical measurements;• To understand the sensitivity of the measurements to CO 2 ;• To recommend the combination of geometries and physics to be used for the pilot project measurements,including notional costs; and• To recommend analysis and measurement technology that needs further development.These objectives were addressed by modelling seismic, electromagnetic and gravity responses of idealised,conceptual models of two recently-approved flagship CCS projects viz. the SW Hub in WesternAustralia and the CarbonNet project in Victoria. Baseline, and several data vintages, each representingthe addition of increasing amounts of CO 2 , were modelled in order to assess the suitability of eachgeophysical method to each flagship project. Geophysical data from different vintages were analysed inorder to establish the sensitivity of each method to CO 2 injection.Because most extant geophysical methods cannot detect it directly, it is clear that no single geophysicalmethod in isolation has the capability to monitor CO 2 . This means that an effective geophysical monitoringand verification strategy should incorporate one or more methods. For particular scenarios, theexact remote sensing combination will vary, but such methods will generally include reflection seismics,electromagnetics or gravity.This project found that:-• Time-lapse surveys are required of all geophysical methods studied in this report. It was notpossible to infer CO 2 saturation from a single geophysical data vintage. The requirement forgeophysical time-lapse surveys is concomitant with establishing high-quality baseline models;• Extant high-quality well logging data are required to build high-quality geological models;• Accounting for uncertainties in seismic modelling improves the ability to evaluate CO 2 saturationand is required for robust risk assessment;• Permanent seismic arrays significantly improve S/N ratios allowing for cost-effective (in the longterm) acquisition of high-quality data with minimal impact to the community;• In shallow (typically < 100 m) water columns, marine electromagnetic surveys would be unlikelyto detect CO 2 variation; and• Because of the falloff in response over distance, gravity and electromagnetic surveys should beconducted downhole on land. These need not be in vertical wells.| iii

This project identified the following analysis and measurement technology with potential for furtherdevelopment:-• Codes which are capable of modelling the geophysical response complex CO 2 distributions, andtheir behaviour over time, are required. The simple models used in this report, particularly forelectromagnetics, are incapable of modelling the complex distributions of a field scenario. Placingmodelling codes in the public domain would increase the transparency of the M&V process.;• Workflows and requirements suitable for synthesising modelling results of multiple geophysical(and other) modelling methods are required in order to place modelling results in proper geologicalcontext;• Further research and development focusing on the quantitative interpretation of geophysical data isnecessary to ultimately provide decision makers with the necessary information about uncertaintiesin potential migration paths and distribution of CO 2 .• Limitations of extant effective medium theories, particularly for electromagnetics, were found.Development of appropriate effective medium theories is required in order to properly couplemultiple geophysical measurement techniques; and• Because of its potential to directly target 13 C, further development of the NMR method is required.Successful development could allow direct measurement of CO 2 saturation in saline aquifers,which is not currently possible. Direct CO 2 measurement would allow models to be calibrated.iv |

Contents1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 CO 2 sequestration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.1 CO 2 Sequestration state and conditions . . . . . . . . . . . . . . . . . . . . . 51.1.2 CO 2 Trapping mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.2 Uncertainties in geophysics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.2.1 Geological uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 CCS In Australia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.1 South West Hub CCS Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.1.1 Prior studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.1.2 Data review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.1.3 Conceptual model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.2 CarbonNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232.2.1 CarbonNet geological setting . . . . . . . . . . . . . . . . . . . . . . . . . . . 232.2.2 Data review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262.2.3 CarbonNet conceptual model . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 Rock physics relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.1 Elastic properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.1.1 Sandstone-shale model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.1.2 Clean sandstone model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.2 Electrical properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373.3 Effective medium theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404 Geophysical remote sensing techniques . . . . . . . . . . . . . . . . . . . . . . . 414.1 Reflection seismic surveying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424.1.1 Seismic noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424.1.1.1 Surface related noise analysis . . . . . . . . . . . . . . . . . . . . . . 424.1.1.2 Quantitative noise analysis . . . . . . . . . . . . . . . . . . . . . . . . 484.1.2 Data processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.1.3 1D Reflection seismics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.1.4 2D Reflection seismics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554.1.4.1 SW Hub Line GA2011-LL1 . . . . . . . . . . . . . . . . . . . . . . . 554.1.4.2 SW Hub Line GA2011-LL2 . . . . . . . . . . . . . . . . . . . . . . . 604.1.4.3 Marine 2D seismics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 664.1.5 3D Reflection seismics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 744.1.6 Permanent receiver installations . . . . . . . . . . . . . . . . . . . . . . . . . 754.1.6.1 Vertical seismic profiling . . . . . . . . . . . . . . . . . . . . . . . . . 754.1.6.2 Ocean bottom cables . . . . . . . . . . . . . . . . . . . . . . . . . . . 794.2 Electromagnetic surveying & interpretation . . . . . . . . . . . . . . . . . . . . . . . 814.2.1 Introductory remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 814.2.2 General comments on induction logging . . . . . . . . . . . . . . . . . . . . . 824.2.2.1 Single-borehole electromagnetics . . . . . . . . . . . . . . . . . . . . 874.2.2.2 Separated-borehole electromagnetics . . . . . . . . . . . . . . . . . . 894.2.2.3 Vertical electric bipole systems . . . . . . . . . . . . . . . . . . . . . 96| v

4.2.3 Marine electromagnetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 974.2.4 Ground electromagnetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1114.3 Gravimetric surveying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1184.4 Other methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1224.4.1 Passive seismic monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1224.4.2 Nuclear Magnetic Resonance . . . . . . . . . . . . . . . . . . . . . . . . . . . 1245 Monitoring & verification strategies . . . . . . . . . . . . . . . . . . . . . . . . . 1275.1 General recommendations & strategies for CCS projects . . . . . . . . . . . . . . . . 1295.2 Recommendations for flagship CCS projects . . . . . . . . . . . . . . . . . . . . . . 1326 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1357 Recommendations for future research . . . . . . . . . . . . . . . . . . . . . . . . 137A CO 2Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A 1B Seismic preprocessing workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . B 1C Seismic data processing for quantitative interpretation . . . . . . . . . . . . . . . C 1C.1 Near-Surface velocity correction to datum . . . . . . . . . . . . . . . . . . . . . . . C 1C.2 Linear noise filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C 3C.3 Deconvolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C 4C.4 Multiple removal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C 4C.5 Amplitude correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C 5C.6 Attenuation correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C 6C.7 Velocity and migration velocity analysis . . . . . . . . . . . . . . . . . . . . . . . . . C 6C.8 Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C 6C.9 Data calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C 7D Geophysical software used in the project . . . . . . . . . . . . . . . . . . . . . . . D 1E Project details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E 1vi |

4.63 Surface gravity response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1204.64 Downhole gravity response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1214.65 NMR Depth of investigation dependency on B 1 frequency and temperature . . . . . 1254.66 NMR relaxation time of methane in the presence of CO 2 . . . . . . . . . . . . . . . 1265.1 Illustration of uncertainty from electromagnetics in the depth to CO 2 migration front 128A.1 Relationship between salinity and conductivity . . . . . . . . . . . . . . . . . . . . . A 2C.1 Schematic illustration of a workflow for production of true-amplitude seismic sections C 2| ix

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List of Tables2.1 Interval velocities for SW Hub Conceptual model 2 . . . . . . . . . . . . . . . . . . 222.2 Interval velocities for Gippsland Conceptual model . . . . . . . . . . . . . . . . . . . 284.1 Survey parameters used for finite difference modelling of seismic responses for theSW Hub model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554.2 Dimensions of CO 2 plumes modelled for SW Hub Conceptual model 2 . . . . . . . . 614.3 Dimensions of CO 2 plumes modelled at Gippsland . . . . . . . . . . . . . . . . . . . 674.4 Advantages and disadvantages of frequency and domains in electromagnetic prospecting. 814.5 Parameter variation for separated borehole modelling study . . . . . . . . . . . . . . 904.6 Target size variation in mCSEM study . . . . . . . . . . . . . . . . . . . . . . . . . 1034.7 LOTEM Gate parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1134.8 Typical densities of aquifer rocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1184.9 Micro seismicity location techniques . . . . . . . . . . . . . . . . . . . . . . . . . . 1225.1 Two general M&V programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1295.2 Suitability of geophysical methods for CO 2 monitoring and verification . . . . . . . . 131A.1 Rock physics parameters used for the Lesueur group . . . . . . . . . . . . . . . . . . A 1D.1 Geophysical codes used in this project . . . . . . . . . . . . . . . . . . . . . . . . . D 1E.1 Report responsibility by section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E 1| xi

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List of abbreviationsM&VMonitoring and verificationCCSCarbon Capture and StorageFOSSFree & Open Source SoftwareS/NSignal to noiseEMElectromagneticsCSEM Controlled source electromagneticsmCSEM Marine controlled source electromagneticsLOTEM Long offset electromagneticsFRPFibre-reinforced plasticRLrelative levelP (wave) Compression (wave)S (wave) Shear (wave)CDPCommon depth pointCMPCommon mid pointOBCOcean bottom cableVSPVertical seismic processingNRMS Normalised root mean square| xiii

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1 Introduction1.1 CO 2 sequestrationThe process of capturing carbon dioxide (CO 2 ) and storing (sequestering) it into deep saline aquifers isbecoming an important method in reducing global atmospheric emissions of CO 2 (e.g. Michael et al.,2009). Aquifers, which are defined as underground layers of water-bearing permeable rock or unconsolidatedmaterials, are chosen (e.g. Doughty et al., 2007; Schilling et al., 2009; JafarGandomi and Curtis,2011) in preference to depleted hydrocarbon reservoirs (e.g. Arts et al., 2004; White and Johnson, 2009),and deep, unmineable coal seams (e.g. Gale and Freund, 2001), primarily because their potential forCO 2 storage is much greater (IPCC, 2005), but also because of a perception of reduced safety in depletedreservoirs because of poorly-sealed abandoned wells. This report considers only sequestration in salineaquifers. Figure 1.1 illustrates the worldwide prospectivity of sedimentary basins for CO 2 sequestration.Figure North American 1.1: Worldwide capacity, oil distribution and gas reservoirs of are sedimentary estimated basins Figure 1and Sedimentary their prospectivity basins showing suitability for CO as sequestrationto be able to contain ~80 Gt C, saline aquifers between 900sites (IPCC 2005)2 sequestrationand 3300 Gt C, and(aftercoalIPCC,beds about2005).150 Gt C, for a totalof about 1160 to 3500 Gt C (DOE 2007). If these estimatesare correct, there is sufficient capacity to sequester severalMonitoring and verification (M&V) of stored CO remain separate. At conditions expected for sequestration,hundreds of years of emissions. Only time and experience 2 is critical to the success of any sequestration projectCO 2 and water are immiscible. Oil and CO 2 may or may not(e.g. will tell JafarGandomi whether these estimates and Curtis, are correct. 2011). Monitoringbe requires miscible, that depending the subsurface on the composition distribution of the of oil CO and 2In the short term, the biggest challenge is match sequestrationsites to CO 2 sources. For example, the large capacity When the fluids are miscible, the CO 2 eventually displacesthe formation pressure. CO 2 and natural gas are miscible.be observed and tracked. Monitoring techniques need to be able to detect when a minimum thresholdin the oil and volume gas reservoirs and saturation will only become in COavailable when the nearly all of the original fluid. Injection of an immiscible2 has been exceeded, either within a storage reservoir or without.operator declares them depleted or implements enhanced oil fluid bypasses some fraction of the pore space, trappingMonitoringrecovery (EOR)therefore,(ARI 2006).isAacomparisondirect driverof sequestrationof the storagesome process, of the and original mustfluid. be carried With the out limited over the exception lifetime ofcapacity and emissions indicates that some of the greatest dry-gas reservoirs, most sequestration projects will requireof the reservoir (Benson et al., 2004).CO 2 emitters (e.g. in the Ohio River Valley, India, and parts immiscible displacement to one degree or another. Forof China) are located in regions without large sequestration example, although oil and CO 2 are miscible, the water thatDespitecapacities.COOn 2 ’sthetransparencyother hand, Texas,to mosttheextantUS stategeophysicalwith the is methods, almost always there present are many in formations conceivable is not geophysicalmiscible withhighest CO 2 emissions, has extremely large sequestration oil or CO 2 /oil mixtures. Equilibration of CO 2 between oilmeasurements that might be made to map the distribution of COcapacity. CCS will likely begin in regions with large emissionmeasuring sources, large acoustic sequestration impedance capacity, contrasts and opportunities using a seismicand water depends 2 . We might derive COon the composition of 2 distributionsthe oil.byUnder conditionssurvey. Wewheremightthe fluidderivephasesCOare 2 distributionsnot miscible,for combining CO 2 sequestration and EOR. Beyond that,by the pressure needed to inject CO 2 , the rate at which theparticularly measuring the differences case of saline in aquifers densityand using coal gravity beds, the surveys. Such differences could arise from changes inleading edge of the CO 2 plume moves, and the fraction ofpressure,scientific foundationsCOand the potential risks of large-scalethe pore space filled with CO 2 are all governed by multiphaseflow relationships (Bear 1972). For CO 2 sequestra-injection must 2 saturation, or both. We might also derive CObe established.2 distributions by measuring changes in conductivityusing electrical or electromagnetic (EM) surveys.tion, threeSuchparticularly measurementsimportanthaveconsequencesformed thearise basisfromofaSCIeNTIFIC number of prior FUNDAmeNTAlSstudies (Kazemeini et al., 2010; Giese multiphase et al., 2009; flow behavior. SchillingFirst, et al., the 2009; fraction Gasperikovaof the poreOF GeOlOGICAl SeqUeSTRATIONspace that can be filled with CO 2 is limited by the flowdynamics and capillary pressure resulting from interactionPhysical Properties of CO 2of two or more phases. At most, about 30% of the pore space | 1The physical state of CO 2 varies with temperature and pressure,as shown in Figure 4a (Oldenburg 2007). At ambient CO 2 saturation is likely to be even less because of buoyancyis filled with CO 2 during initial displacement. In practice,conditions, CO 2 is a gas, but it becomes liquid at greater and geological heterogeneity, both of which cause portionsdepth. At high temperature, CO 2 is a supercritical fluid of the formation to be bypassed. After injection has stopped,when pressure is high enough. The transition from one CO 2 continues to move and fluid saturation approachesstate to another depends on the geothermal gradient. In equilibrium, which is determined by the capillary pressure

and Hoversten, 2006, 2005; Wells et al., 2006; Sherlock and Dodds, 2003). These studies found thathigher resolution was achieved using seismic methods and that gravity, electrical and electromagneticmethods were also generally applicable, though with lower resolution than seismics. This is not unexpectedgiven the physics that underlies each method. It is important to understand that all these methodswork better in time-lapse mode; it is easier to detect changes due to CO 2 movement than its absolutelocation.In addition, there are more tenuous measurements that might be made. Piezoelectric sensors in boreholesmight record stress on a reservoir seal. Magnetic-tensor gradiometry might be used to recordchanges in magnetic strata as CO 2 is injected, but sedimentary basins rarely contain magnetic mineralsin appreciable amounts (in contrast to the basements of sedimentary basins which are often strong magneticsources). We might also include a radioactive tracer with CO 2 and map variations in radiometricresponse, though such an approach would have environmental repercussions.Although they are generally successful in mapping CO 2 distributions, seismic, EM and gravity methodsare not without cost. Gravimetric methods are limited by lower resolution which is generally addressedby moving closer to the source (Gasperikova and Hoversten, 2008). In a CO 2 context, this means usinga borehole gravity meter and the current generation of field instruments is unstable at the pressuretemperatureregimes at which CO 2 is injected. Electrical methods generally require contact betweenthe instrument and the medium. Although this is difficult to achieve with cased boreholes, significantprogress was described by Schmidt–Hattenberger et al. (2011) who suggest placing electrodes on theoutside of FRP casing. Capacitative sensors (Mwenifumbo et al., 2009) might be used with appropriatecasings, but such instruments are currently in their infancy. EM surveys can be used with chromium steelcasedboreholes (Bhatti et al., 2007), but perform better with fibre reinforced plastic cased holes. Currentgeneration tools are limited to about 300 m between cased holes and about 1000 m between uncasedholes (Al-Ali et al., 2009). Rapid fall-off rates for EM signals suggest the use of downhole methodsalthough controlled source electromagnetic (CSEM) methods (Constable and Srnka, 2007) might beused in marine environments. Although CSEM methods have been used for petroleum exploration onland (Strack and Vozoff, 1996), they lack the resolution to be used as a monitoring and verification(M&V) method. Orange et al. (2007, 2009) suggest that marine CSEM is a viable M&V method ifreceivers can be accurately positioned and near-seafloor resistivity variations accounted for.This would seem to leave seismics as the only viable choice to monitor CO 2 distributions. However,seismic methods can be intrusive to landowners and expensive, especially when used in repeated 3D(also known as 4D) mode (Lumley, 2001). Another issue with seismics is their reduced sensitivity tochanges in saturation at high CO 2 saturations. Figure 1.2 extends work by Lee (2004) and comparesseismic (green) response with two forms of EM response for a variety of gas concentrations in marinesaturated sediments. Seismic response is sensitive to changes in gas saturation when saturationis less than about 15%. In contrast, EM is relatively insensitive to changes in gas saturation below90%. In Figure 1.2, Archie’s (blue) and HS (red) refer to Archie’s law (Archie, 1942) and the lowerHashin-Shtrikman bound (Hashin and Shtrikman, 1962) for determining electrical resistivity from porosity.These issues not withstanding, seismics remain the preferred method of imaging the subsurface forCO 2 distribution. For cost and other reasons outlined above, we explore other complimentary methods.2 |

Figure 1.2: Sensitivity to gas saturation of seismic and EM methods in marine saturated sands (afterConstable and Key, 2009). Seismic methods show diminished sensitivity to gas saturationsabove 20%. In contrast, EM methods are particularly sensitive to gas saturations above90%. Employing both EM and seismic methods allows determination of a range of gasconcentrations. The red and blue traces plot against the left axis while the green trace plotsagainst the right axis. Sensitivities in this figure are more applicable to Section 2.2 than toSection 2.1.| 3

Fundamentally, CO 2 detectability in a monitoring scenario will depend on lithology, geological structure,the rock physics relationships in the reservoir, noise levels of measured data and on the saturation level.All of these factors have uncertainties, but although these uncertainties influence CO 2 detectability, theyare rarely taken into account. Monitoring frameworks that do not account for uncertainties provide verylittle information about either the CO 2 distribution or the reliability of that distribution, both of whichare critical when making decisions which affect a sequestration program.The situation is further complicated when different measurements are used. The goal of a monitoringprogram is to map the spatial and thickness distribution of CO 2 to a desired accuracy over time. However,because different measurements are influenced in different ways by CO 2 , each inversion result is anincomplete part of the whole. Resolution of these different inversion results is most efficiently doneusing a model-based inversion, and current trends in geophysics are to derive families of models that fitmultiple data sets. These families are evaluated probabilistically to give a family of most likely models aswell as similar models. Because model evaluation is probabilistic, confidence can be attached to invertedCO 2 distribution.This report is concerned with geophysical M&V of CO 2 sequestered into saline aquifers. After introducingtwo Australian CCS projects, we discuss the rock physics relationships that permit realisticgeophysical modelling of particular environments. We then discuss the application of various geophysicaltechniques in general as well as to conceptual models of the two Australian CCS projects. Finally,we outline some geophysical M&V strategies in reference to conceptual models of the Australian CCSprojects. We begin with a discussion of the general conditions under which CO 2 is sequestered.4 |

1.1.2 CO 2Trapping mechanismsIt is difficult to begin modelling geophysical responses of CO 2 without some consideration of how it istrapped. Typically, four mechanisms are considered and these are discussed in turn. These mechanismsare represented graphically in Figure 1.5 in terms of contribution to trapping over time after injectionceases. Consideration of times up to 10 000 years is similar to risk planning employed by the nuclearindustry. To place this in context, roughly 11 000 years have passed since the end of the last ice age.Such a long-term strategy, specific to CO 2 , was presented by Polson et al. (2012).mONITORING The mIGRATIONAND FATe OF INjeCTeD CO 2Every sequestration project is likely to use a combinationof monitoring techniques to track CO 2 -plume migrationand assess leakage risk. Technology for monitoring undergroundsites is available from a variety of other applications,including oil and gas recovery, natural gas storage, liquidand hazardous waste disposal, groundwater monitoring,food and beverage storage, fire suppression, and ecosystemmonitoring. Many of these techniques have been testedat the three existing sequestration projects and at manysmaller-scale pilot projects around the world (e.g. Arts etal. 2004; Hovorka et al. 2006). Specific regulatory requirementsfor monitoring have yet to be established. Table 1provides examples of two programs that could be deployedto assure project performance and guard against safety andenvironmental hazards (Benson et al. 2005).Geophysical MonitoringSeveral methods can be used to observe the migration ofthe CO 2 plume. Seismic imaging can detect changes incompressional-wave velocity and attenuation caused bythe presence of CO 2 . Electromagnetic imaging can detectdecreases in electrical conductivity when CO 2 is present inrock pores as a separate phase. Gravity measurements aresensitive to the decrease in bulk-rock density when CO 2is present. To date, seismic imaging has been used mostextensively and with great success.Figure 7 shows a sequence of seismic cross sections collectedfrom the Sleipner project. The first image, from 1994, wasobtained before injection started. Only two major reflectionsare evident, correlating with the top and bottom ofthe Utsira Sand. By the first post-injection survey in 1999,three years after injection began, about 3 million tons ofCO 2 had been injected. Several new reflections are present,which are interpreted to represent CO 2 trapped within thepores of the Utsira Sand. The plume is about 1 km wide.Subsequent images show continued plume growth as moreCO 2 is injected.FigureFigure 1.5: Schematic representation 6A general representation of the evolution of mechanisms of theover security time (IPCC of 2005). COActual 2 trapping mechanisms over time (after IPCC,mechanisms and evolution vary from site to site.2005). Different sites have different combinations of these trapping mechanisms.Geological structures are the most common trapping mechanism when CO 2 is sequestered in depletedhydrocarbon reservoirs. Because of depositional environments, structural trapping is also appropriate forBasic monitoring programenhanced monitoring programdeep saline aquifers considered in this report. COPre-operational monitoring 2 is trapped when fine-textured relatively-impermeablePre-operational monitoringrock impedes upward migration of injected CO 2 due to buoyancy. Structural trapping is the intendedTable 1MONITORINg PROgRAMS ThAT COULD bE USED OVER ThELIfETIME Of A SEqUESTRATION PROjECT (AfTER bENSON ET AL. 2005)Well logsWell logsSeismic imaging can also be used in other geometric configurations,such as betweenWellhead pressureWellhead pressuremechanismtwo or moreforwellsthe(cross-wellproject discussed formation pressure in Section 2.2 of formation thispressurereport and is employed in the CO2CRC’simaging) or with a combination of surface sources and boreholesensors (vertical seismic profiling). These higher-reso-Seismic surveySeismic surveyInjection- and production-rate testing Injection- and production-rate testingOtway Pilot project (CO2CRC, Atmospheric-CO 2011),lution methods have been applied with success at several2Ketzin monitoring (Forstergravity et al., survey 2006) and at Sleipner (Arts et al., 2004).Electromagnetic surveypilot-scale CO 2 injection tests (Hovorka et al. 2006).Atmospheric-CO 2 monitoringWhen injection stops, formation water imbibes into CO the 2 -flux CO monitoringGeochemical MonitoringPressure and water 2 plume slowing progress of the plume’squality above thestorage formationTwo approaches can be trailing used to monitor edge. COThis 2 injection. process The is known as capillary or residual-phase trapping. In the absence of a structuralfirst uses fluid samples collected from observation wellsOperational monitoringOperational monitoringwhere changes in brine trap, composition capillary or the trapping presence isofconsidered to be the most important trapping mechanism, and is the most likelyintroduced or natural tracers are monitored. The second Wellhead pressureWell logsmonitors the near-surface mechanism for CO 2 leakage. for the project discussed Injection and production in Section rates 2.1 of Wellhead thispressurereport. It has been suggested (Hesse et al.,Wellhead atmospheric-CO 2 monitoring Injection and production ratesBy far the most rapid and inexpensive on-site measurement MicroseismicityWellhead atmospheric-CO 2 monitoring2009; Ide et al., 2007) that with Seismic enough surveys time, all injected Microseismicity COtools available to aid in tracking the injected CO 2 and its2 can be trapped using this mechanism.Seismic surveybreakthrough to observation wells are pH, alkalinity, and gasgravity surveycomposition. Of these, Solubility pH is probably trapping the most diagnostic occurs when CO Electromagnetic surveyindicator of brine–CO 2 interaction. A marked decrease in pH2 is dissolved in formation water. The success of this trapping mechanismCO 2 breakthrough. depends The on compositions the balance between pore pressure and storage temperature formation and salinity. Solubility of COContinuous CO 2 -flux monitoringPressure and water quality above thecorrelates directly withof major, minor, and trace elements can be used to assess2the extent of water–COincreases 2 –rock interactions. as pressure Enrichment increases ofClosure but monitoring decreases as both temperature Closure monitoring and salinity increase. The attraction ofconstituents such as Fe, Mn, and Sr can indicate mineraldissolution at depth during thisreaction trapping of COmechanism 2 -saturated brine is that Seismic CO surveySeismic surveywith rock (Emberley et al. 2005; Kharaka et al. 2006a, b).2 is trapped as a liquid which sinks under gravity rather than a gasgravity surveyElectromagnetic surveyTracer studies are important for in situ subsurface characterization,monitoring, and validation. Naturally occur-Pressure and water quality above theCO 2 -flux monitoringstorage formationring elements, such as the stable isotopes of light elements( 18 O, D, 13 C, 34 S, 15 Wellhead pressure monitoringN), noble gases (He, Ne, Ar, Kr, Xe), andElEmEnts 329OctOber 2008| 7

which rises. Eke et al. (2011) recommend this trapping mechanism as a way to overcome CO 2 ’s naturalbuoyancy.Lastly, CO 2 is trapped when it reacts with formation minerals precipitating carbonate minerals. Thistrapping mechanism is attractive because of its security, though the time over which CO 2 is mineralisedis unclear.From the brief discussion of trapping mechanisms, it is apparent that a sequestration project will notrely on a single mechanism. This suggests that different geophysical measurements will be required atdifferent stages of project. For example, mapping displacement of conductive fluids during the initialstages of a project is a very different problem to mapping conductive fluids in a carbonate aquifer at verymuch later stages. In turn, this suggests the requirement for time-lapse surveys over the life of the projectto ensure that CO 2 is being trapped rather than escaping. A common benchmark for CO 2 sequestrationfor climate mitigation purposes is less than 1% leakage in 1000 years.8 |

1.2 Uncertainties in geophysicsImaging the subsurface to find suitable reservoirs to store CO 2 , deriving the distribution of CO 2 in areservoir or estimating the available volume for storage of CO 2 can all be considered as geophysicalinverse problems. That is, given some indirect observation at the surface or in boreholes, the goal is toderive properties of the subsurface. Uncertainties in the derived properties are a consequence of nonuniquenessof geophysical inverse problems. Non-uniqueness is a fundamental property of geophysicalinverse problems (Backus and Gilbert, 1967) and means that if any model can be found that fits the datawithin the uncertainties, then an infinite number of alternative models exist that fit the data equally well.Traditionally, little-to-no attention has been devoted to the uncertainties in derived properties; the focushas been on finding a single best fitting model.Bayesian approaches to inverse problems, such as estimating the saturation of CO 2 in the subsurfacefrom seismic data recorded at the surface, aim at recovering the distribution of saturation values that arein agreement with prior information and the data. The available states of information are described byprobability density functions. Bayes theorem (Bayes, 1763) relates the prior distribution p(m) of modelparameters, m, the likelihood function L(d|m) of data, d, given model parameters, m, and the posteriordistribution P (m|d), the solution to the inverse problem.P (m|d) ∝ p(m)L(d|m) (1.1)The prior distribution describes the range of plausible models based on prior information and the likelihoodfunction favours models with a good fit to the data over models with a poor fit to the data. TheBayesian perspective applied in this report where possible, stands in a stark contrast to the establishedpractice in geophysics where the aim is to find one single best fitting model often with the help of regularisationand damping. Clearly, in any CO 2 monitoring scenario, a single best fitting model is of littlevalue for the decision makers. A comprehensive quantitative assessment of the risks associated with areservoir is only possible if we can analyse the posterior distribution which is the distribution of plausiblemodels that are in agreement with the data. However, the development of algorithms to recover posteriordistributions instead of the single best fitting model in a practical setting is far from trivial.Geological models such as the conceptual models developed in this study contain different types ofuncertainty. How these various types of uncertainties are best described and derived is a problem in itself.Recently Bayesian methods have gained a lot of attention as an appropriate tool to find the distributionof geological models that are in agreement with various geophysical datasets on a variety of scales.1.2.1 Geological uncertaintyGeological uncertainties can be divided into three categories.• Structural uncertainties are uncertainties associated with the boundaries of lithological units, e.g. thespatial location of faults and interfaces;• Lithological uncertainties are uncertainties associated with properties of a given geological unit,e.g. the distribution of net-to-gross values within a unit. The term ’net-gross’ refers to the amountof reservoir (net) within a geological unit (gross); and| 9

• Rock physics uncertainties are uncertainties associated with the underlying rock physics model.Well-derived trend curves are an approximation to the true Earth and well logs themselves containnoise.Section 3.1.1 shows how rock physics uncertainties can be accounted for, and Section 4.1.3 shows howlithological and structural uncertainties can be accounted for and the implications for CO 2 sequestration.However, a full description of structural and lithological uncertainty requires a model of uncertainty thattakes spatial correlation into account. A variogram describes not only the uncertainty for an individualdata point but also the amount and direction of spatial continuity of a data set. Given a variogram, asequential Gaussian simulation (Deutsch and Journel, 1997) can be used to generate multiple realisationsof the random field and thus the range of plausible distribution of, for example, net-to-gross valueswithin a structural unit. We use sequential Gaussian simulations to both perturb the layer boundaries inthe conceptual model and assign distributions of net-to-gross values to the individual geological units.By allowing for an anisotropy in the underlying variograms we can achieve a layering of the net-to-grosswithin the geological units. Figure 2.3(b) shows a realisation of a conceptual model where sequentialGaussian simulations are used to populate the layers with net to gross values and to perturb the layerboundaries. Given net-to-gross values, we can derive elastic and electrical properties using the appropriaterock physics framework described in Section 3.1.1. An example of this is shown in Figure 1.6.0horizontal distance (km)0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16elevation (km)−1−2−3−4(a) Net to gross realisationnet to gross1.000.950.900.850.800.750.700.650horizontal distance (km)0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16elevation (km)−1−2−3vp (km/s)4.54.03.53.02.5−4(b) Derived V P distribution2.0Figure 1.6: Example of a sequential Gaussian simulation. a) A sequential Gaussian simulation is usedto populate the geological units with net-to-gross values and perturb layer boundaries by upto 5%. An effective medium theory (see Section 3.1.1) is required to generate a physicalpropertymap for a particular realisation. Figure b) shows such a map for V P .10 |

2 CCS In AustraliaCurrent and proposed Australian CCS projects are shown in Figure 2.1. Of the 18 projects, three areconsidered to be flagship or potential flagship projects. Because of the large range of sites, the projecthas restricted its attention to two sites. Both the SW Hub and Gippsland Basin (shown as CarbonNet)projects are considered flagship projects (Department of Resources Energy and Tourism, 2011a). Thethird potential flagship project (Wandoan) has elements of the SW Hub and CarbonNet projects viz. theonshore nature of the SW Hub and the conventional seal of CarbonNet.Mention must also be made of the CO2CRC Otway project (CO2CRC, 2011). The CO2CRC OtwayProject is Australia’s first demonstration of the deep geological storage of CO 2 . The project providestechnical information on geosequestration processes, technologies and monitoring and verificationregimes that will help inform public policy and industry decision-makers while also providing assuranceto the community that the geosequestration process is viable. The project has been running since 2003and sequesters CO 2 in both a depleted gas field and a saline aquifer.Figure 2.1: Australian CCS projects. The Collie SW Hub project and the CarbonNet project are discussedin this report. The Collie SW Hub project has been known as the Lesueur Project,but is now called the SW Hub project.This Section discusses the rationale for choice, geological setting and limitations of particular conceptualmodels used to model geophysical responses in Section 4.| 11

2.1 South West Hub CCS ProjectThe South West Hub (SW Hub) CCS project is located near Collie in south west Western Australia.In addition to being the first CCS flagship to be approved, the SW Hub was chosen as a study areabecause of its novel trapping mechanism. Capillary trapping was introduced in Section 1.1.2, and willbe discussed in Section 2.1.3.115˚30'−32˚30'Pinjarra 1116˚00'Perth−32˚30'Core is from Pinjarra 1well 31km N of mostrecent seismic data010002000depth (m)3000Lake Preston 14000−33˚00'AHarvey 1B−33˚00'50000 2000 4000 6000 8000in−situ vp (m/s)Most recent wireline data is from LakePreston 1, 18km NNW of Harvey 1(sand sections have been highlighted in red)AB0trace100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 140012time (s)3−33˚30'115˚30'116˚00'−33˚30'45most recent seismic lines acquired in 2011Figure 2.2: SW Hub Project regional context. Modern (2011) seismic lines are shown in violet. Thelocation of two wells, Pinjarra 1 and Lake Preston 1, are indicated as red circles. The locationof the Harvey-1 exploratory data well which was drilled between January and February,2012, is indicated in orange. The seismic data are from GA2011-LL1, the northernmostseismic line.2.1.1 Prior studiesThe major motivation for investigation of the SW Hub as a CCS project comes from work by Varmaet al. (2009) who interpreted a high permeability zone from well log data. Their work lead to the commissioningof a preliminary study (Barclay et al., 2009). Some of the quantities (e.g. formation watersalinity and injection pressure) are used in this report, but most others, chiefly because of Barclay et al.(2009)’s focus on geomechanical aspects of the SW Hub, were not. As work proceeds on the SW Hubproject, geological, geochemical and geomechanical data will no doubt be incorporated into a reservoirmodel which can be used to numerically simulate injection strategies and project lifecycle, but such integrationis beyond the scope of the current study and not possible given the current lack of data. Varmaet al. (2009)’s interpretation is reproduced in Figure 2.3. It is important to note that Varma et al. (2009)’sconcept model is oriented North-South in contrast to SW Hub models in this report which are orientedWest-East.12 |

NYarragadeeLeederville aquiferInjection wellYarragadeeSCockleshell GullyUpper LesueurImmobilised CO 2Secondary percolationLower LesueurPrimary migrationSabina sandstoneInjection ∼ 3 km(b) SW Hub Concept model (not to scale)Figure 2.3: Motivation for selection of the SW Hub site. Figure 2.3(a) shows resistivity logs from LakePreston 1 well which Varma et al. (2009) interpreted as indicative of fresh water recharge.Figure 2.3(b) presents Varma et al. (2009)’s concept model. The concept model is orientedNorth-South. The injection well is at the right of the figure. CO 2 is injected intothe relatively-permeable Lower Lesueur formation. As the plume migrates upwards, CO 2 istrapped by capiliary mechanisms. These mechanisms were discussed in Section 1.1.2.| 13

2.1.2 Data reviewPublically-available data for the SW Hub project span from the early 1960’s to the present day. A firstset of seismic lines had been collected and processed in the early 1960’s and 1970’s, and some of theselines were reprocessed in the early 1990’s. The majority of these lines lack any form of navigationaldata and have extremely low S/N ratios due to the data having been acquired with low fold and largegroup intervals. Data quality is further reduced by the unfavourable surface conditions as a result ofcoastal limestones and sand dunes. Lines shot in the dipping direction have generally better S/N ratiosthan lines shot along the strike of major N-S faults. Given that new seismic lines were collected in 2011(Figure 2.4), we used one of those lines (GA2011-LL1) to derive a conceptual model that is characteristicfor the situation in the SW Hub Project target region. None of the seismic data were processed withthe aim of obtaining of true-amplitude migrated sections that would be necessary to tie the seismic tothe existing wells. The aim of the seismic processing has been to provide much-needed images of thestructure.None of the existing well logs intersect the new seismic lines. The closest well is Lake Preston 1 nearthe eastern starting point of the seismic Line GA2011-LL1. The nearest core is in the Pinjarra 1 well,several kilometres north of the SW Hub Project. It is hoped that the paucity of well-logging data willbe addressed by the Harvey-1 well which was drilled between January and February, 2012. This wellwill help tie the seismic lines to well information, once the data have been reprocessed to produce trueamplitude migrated sections. The term ’true amplitude’ has different meanings in seismic processingdepending on application. We refer to true amplitude migrated (Schleicher et al., 1993; Zhang et al.,2001; Ekren and Ursin, 1999, amongst others) sections as seismic sections that have been processedso that reflector amplitudes relate directly to the change in rock properties that cause them – primaryreflections that are freed of their geometrical spreading loss.Regional potential field data over the SW Hub were interpreted by Iasky and Lockwood (2004). Theyshow the Harvey Ridge as a basement high in a moderate anomalous gravity low. Regional magneticintensity data are largely featureless in the SH Hub region. Depth to basement modelled from gravity ispresented in Figure 2.5.14 |

GA2011-6GA2011-4GA2011-5GA2011-3GA2011-2EGA2011-1ZFigure 2.4: 2011 Seismic survey data for SW Hub. The view is from the North looking South as indicatedby pointers at the lower right of the image, and line numbers are indicated. LinesGA2011-1 to 3 are oriented East-West while lines GA2011-4 to 6 are oriented South-North.The conceptual models are based on Lines GA2011-1 and GA2011-2.| 15

GSWA Record 2004/8Gravity and magnetic interpretation of the southern Perth Basin, W.A.32°Challenger 1Bouvard 1Pinjarra 1Felix 1Sugarloaf 1LakePreston 1Preston 133°Wonnerup 1Sabina River 1Chapman Hill 134°Whicher Range 1Whicher Range 2Whicher Range 4WhicherRange 3Rutile 1Sue 1Blackwood 1Alexandra Bridge 1Scott River 1Canebreak 1Petroleum wellsWarrenRiver 3Warren River 1Warren River 2Gas35°StratigraphicGas, plugged andabandonedDry, plugged andabandonedCoastlineTectonic boundary1-1Interpreted lineament-550 km115° 116° 117°RPI318 23.03.04Figure 2.5: Modelled depth to basement for southern Perth Basin from gravity data (after Iasky andaLockwood, 2004). The SW Hub CCS project lies in a broad basement (relative) high nearthe Harvey Ridge in the upper centre of the image near the Preston 1 well. The HarveyRidge is the diagonal trend South of Preston 1 well. These depths are within 20% of thosederived from legacy seismic data.16 |km-9

2.1.3 Conceptual modelThe South Perth Basin trending along the southern end of the western Australian coast is formed by aPermian to Holocene succession of sediments overlying a Precambrian basement. The basin’s easternboundary is marked by the Darling Fault and the Yilgarn Craton while its western boundary is markedby a thinning of the sedimentary sequence toward oceanic crust in deeper water.The structurally complex Perth Basin was formed during the separation of Australia and Greater Indiain the Permian to early Cretaceous (Crostella and Backhouse, 2000). It includes a significant onshorecomponent in the South and in the North extends offshore to the edge of the continental crust in waterdepths of up to 4500 m. Sandier facies in the South Perth Basin (Sabina Sandstones) suggest that thebasin was closed in the early Triassic and being filled from the South. Further subsidence of the basinlead to regional marine transgression in the early Triassic (Wonnerup) followed by a regression to deltaicand fluvial facies (Myallup) throughout the middle to late Triassic. Triassic rivers flowed from the southor southwest and are assumed to have covered both the Perth Basin and much of the Yilgarn Craton.In the early and middle Jurassic fluvial and marshy sedimentation was white spread in the South. Inthe late Jurassic continental sedimentation was again widespread. The ongoing northwest-southeastextension from the middle Jurassic to earliest Cretaceous culminated in the break-up of Australia andGreater India. The break up in the early Cretaceous was associated with widespread uplift and erosionand possibly also with volcanism. A renewed phase of subsidence followed, where localised saggingallowed for the deposition of submarine sediments, including turbidites. The geological setting of theSW Hub is illustrated in Figure 2.6. Correlation of geological facies across the basin is presented inFigure 2.7.The SW Hub project focuses on the Harvey ridge, a faulted anticline structure 47 km north of Bunburythat forms the boundary between the Bunbury Trough and the Mandurah Terrace. The structure showssigns of four-way dip closure but is cut by several large faults and was first identified by gravity surveys.Lake Preston 1 is currently the closest well and was drilled on the western flank of the anticline.Because of the paucity of adequate data in and around the SW Hub, it does not make sense to constructa complex model. Instead, we focus on conceptual models which capture salient geological features.There are a number of possibilities for creating such models. If we make the decision to base the modelon existing core and available well data, then the conceptual model should be based on GA2011-LL1, thenorthernmost of the recent vintage seismic lines. However, if we decide to base the conceptual model onthe 2012 Harvey 1 well location and core, then the conceptual model should be based on GA2011-LL2,the next most southern line. It is important to note that at the time of writing, data from this well was notpublically available. Figure 2.8 compares seismic data from GA2011-LL1 and LL2. Although there isa similarity between structures that could reasonably be interpreted on both lines, there are differencesalso. These differences strongly influence the construction of the conceptual model.| 17

−32˚30'115˚30'116˚00'−32˚30'MandurahTerracePinjarra 1Lake Preston 1Harvey RidgeDarling faultAB−33˚00'Harvey 1Yilgarn Craton−33˚00'well (Feb 2012)−33˚30'115˚30'Bunbury Troughwellseismic linebasin depth contourfaultanticline116˚00'−33˚30'Figure 2.6: Simplified geological map of the South West Hub. Seismic lines are 2011-vintage. Basementtopography was derived from WAIMPS and Crostella and Backhouse (2000). The target areafor the South West Hub CCS project falls to the east of the apex of the Harvey Ridge.18 |

(a) Photo of core from 625 m (b) Photo of core from 1385 m (c) Photo of core between 2586 and2587 mCrostella and BackhouseS NPERTH BASINBootine 10Warnbro GroupMain unconformityParmelia GroupBullsbrook 1Yarragadee Formation100Cadda Formation500Cattamarra Coal MeasuresEneabba Formation500SOUTHERN PERTH BASINNORTHERN PERTH BASIN1000CRETACEOUSJURASSICLesueur Sandstone1000Cockburn 10Woodada FormationWonnerup MemberKockatea ShaleSabina SandstoneTRIASSIC1500Beekeeper FormationWillespie FormationRedgate Coal MeasuresCarynginia Formation1500500Sue Group2000PERMIAN20002500Rockingham 10 1000Irwin River Coal MeasuresHigh Cliff SandstoneHolmwood ShaleNangetty Formation (not present)Basement2500PRECAMBRIAN PERMIAN TRIASSICMyalup MemberLesueurSandstoneAshbrook SandstoneRosabrook CoalMeasuresWoodynook SandstoneMosswood FormationBasementWoodada 3PRECAMBRIAN150050003000Pinjarra 10Top CattamarraCoal Measures300020001000500350050035001000Peron 1040001500 2500(TD = 1563 m)10004000LakePreston 10Wonnerup 1015003000WhicherRange 105001500(TD = 3054 m) (TD = 4257 m)(TD = 4306 m)?50050020005001000(d) S-N well strata correlation from wells across the South Perth Basin2000100010002500(TD = 2522 m)10001500?250015001500?15002000300020002000?200025003500(TD = 2600 m)250025002500Sue 1400003000?3000?300045005003500(TD = 4572 m)3500115°00' 116°00'Woodada 33500Peron 1? ’northern’Perth Basin30°00'1000Top WillespieFormation4000400040001500(TD = 4562 m)Bootine 14500INDIANBullsbrook 1OCEAN4500(TD = 4727 m)’central’200032°00' Perth BasinPERTH(TD = 4653 m)?Cockburn 1Rockingham 1?Pinjarra 1Lake Preston 12500BUNBURY?’southern’Wonnerup 1Perth BasinWhicher Range 1300034°00'Sue 150 km(TD = 3077 m)AC236a 22.03.008Figure 2.7: The SW Hub concept. Figures (a), (b) and (c) are photographs of core from the Pinjarra 1well at indicated depths. Photographs are oriented so that downwards points to the rightof the page. The scale of each photograph is given by the 3 " diameter core and the tray.Figure (a) illustrates interleaved fine-grained standstone and shale at 625 m. Figure (b) illustratesfine-grained standstone overlying shale at 1385 m. Figure (c) illustrates shale overlyingvery fine grained sandstone between 2586 and 2587 m. Figure (d) shows South-to-Northstrata correlation over the South Perth Basin. This figure illustrates the trapping mechanismof interleaved sands and shales which extend to at least 2600 m below surface. Below thisdepth are the Lesueur sandstones, and example of which is illustrated in Figure 3.2. Thelocation of the Pinjarra 1 well was indicated in Figure 2.6.| 19

0cdp100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 140012time (s)345(a) GA2011-LL10cdp100 200 300 400 500 600 700 800 900 1000 110012time (s)345(b) GA2011-LL2Figure 2.8: Seismic data from GA2011-LL1 and LL2. Line GA2011-LL1 was used to derive SW HubConceptual model 1 while Line GA2011-LL2 was used to derive the second SW Hub Conceptualmodel. There are similarities between these two sections though it is possible tointerpret more structure from GA2011-LL1 .20 |

Because of these differences, we chose two viable conceptual models for the SW Hub. The first conceptualmodel for the SW Hub project (Figure 2.9) is based on a structural interpretation of the northern-mostEast-West seismic line (GA2011-LL1) across the Harvey Ridge. Of the new seismic data, this line hasthe best S/N ratio when compared to the existing seismic lines, a greater amount of geological structureand intersects a likely (Barclay et al., 2009) injection site at about 10 km horizontal distance. Thisconceptual model will be used in Sections 4.1.3 and 4.1.4 of this report.A0Westhorizontal distance (km)East0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16B−1elevation (km)−2−3Tertiary sedimentsJurassic sedimentsMyallupWonnerupSabina sandstones & Sue group1D model location−4Figure 2.9: South West Hub Project conceptual model 1. This model was derived from line GA2011-LL1, the northern most seismic line AB in Figure 2.2. This report takes the region around10 km as the most likely injection site. Seismic data from GA2011-LL1 were shown inFigure 2.8(a).The second conceptual model is illustrated in Figure 2.10. From Figure 2.6, this is most central of theWest-East 2011-vintage seismic lines and it intersects the Harvey-1 well. The veracity and applicabilityof this model should be proven once data from this well are available. This conceptual model will beused in Sections 4.1.4 and 4.1.6.1 of this report. Since these sections deal with 2D surface and boreholeseismic monitoring, we choose to include more structure in the model by including interpreted subdivisionof Jurassic into Eneabba, Cattamarra, and Cadda formations, and adding Cretaceous sediments(Wanbro).To construct the model, the interpreted horizons were converted from time to depth using interval velocitiesobtained from the migration velocities. The corresponding interval velocities are summarisedin Table 2.1. The P-wave velocities are well correlated with log data from Lake Preston 1 well. Thedensities of the interval were obtained using Gardner’s empirical relation Gardner et al. (1974). Sincewe do not have direct measurements of S- wave velocities, these were derived using Castagna’s equation(mudrock line).| 21

W Figure 2.10: South West Hub Project conceptual model 2. This model is derived from line GA2011-LL2 (Figure 2.8(b)) which is the most central of the East-West lines in Figure 2.2. Seismicdata from GA2011-LL2 were plotted in Figure 2.8(b). The grid spacing is 1km×1km. Thered dashed rectangle indicates the subset of the model that is used in the remainder of thiswork.Table 2.1: Interval velocities for the 2D model shown in Figure 2.10.Formation V P (km/s) V S (km/s) Density (g/cm 3 )Superficial sediments 2.2000 0.7241 2.1203Wanbro Group 2.2500 0.7650 2.1323Cadda 2.4000 0.8940 2.1670Cattamarra 2.6704 1.1266 2.2256Eneabba 3.4294 1.7793 2.3692Myalup 4.3330 2.5564 2.5119Wonnerup 4.9830 3.1154 2.601222 |

2.2 CarbonNetThe CarbonNet CCS Project is very different to the SW Hub CCS Project. It is located offshore and hasa conventional reservoir seal.2.2.1 CarbonNet geological settingThe Gippsland Basin is an east-west trending rift basin, located in southeastern Australia. Roughly twothirdsof the basin is offshore, in water depths ranging from 100 m to 4 km. It is a mature hydrocarbonbasin, with onshore exploration commencing in the 1920’s and offshore exploration in the 1960’s. Themajority (roughly 90%) of the oil and gas fields are trapped in structural closures formed in the coarseclastics at the top of the Latrobe Group, beneath the Lakes Entrance Formation regional seal.Gippsland Basin is illustrated in Figure 2.11. Gibson–Poole et al. (2006) list 11 opportunties for CO 2storage in either deep saline formations or depleted hydrocarbon reservoirs in the offshore Gippslandbasin. Several of these opportunities use the Latrobe group. One of these opportunities is the Seaspraydepression CO 2 concept (Aldous, 2010) which is illustrated in Figure 2.12.TheFigure 2.11: The Gippsland basin showing major tectonic elements and extant hydrocarbon fields (afterGibson–Poole et al., 2006).The east-west trending Gippsland Basin was formed as a consequence of the break-up of Gondwana inthe latest Jurassic/earliest Cretaceous (Willcox et al., 2001). The deposition of several major, basin-scalesequences which range in age from Early Cretaceous to Neogene. That these sequences are are boundedby basin-wide angular unconformities reflects the strong tectonic control on the sedimentary developmentof the basin. Other unconformities and disconformities can only be recognised using biostratigraphic agedeterminations to delineate the missing sections. This is of particular relevance in the context of the upperLatrobe Group which contains complex sedimentary sequences that developed at different time intervalsfrom extensive channel incision and subsequent infill processes.| 23

Figure 2.12: The Seaspray depression concept (after Aldous, 2010). There are a number of sequestrationopportunities within the Latrobe group.In the Early Cretaceous, the initial Gippsland Basin architecture consisted of a rift valley complex whichwas composed of multiple, over-lapping to isolated, approximately east-west trending half-grabens. Continuedrifting into the Late Cretaceous generated a broader extensional geometry which consisted of adepositional centre flanked by fault-bounded platforms and terraces to the north and south. The Rosedaleand Lake Wellington Fault systems (Figure 2.11) mark the northern margin of the Central Deep andNorthern Terrace respectively, with the Darriman and Foster Fault systems defining the southern marginof the Central Deep, and the northern boundary of the Southern Platform respectively.Initial rifting in the Early Cretaceous produced a complex system of graben and half-graben into whichthe volcanoclastic Strzelecki Group was deposited. Between 100 and 95 Ma, a phase of uplift andcompression, produced a new basin configuration and provided accommodation for large volumes ofbasement-derived sediments. Renewed crustal extension during the Late Cretaceous, established theCentral Deep as the main depositional centre. Initial deposition into the evolving rift valley was dominatedby large volumes of material that were eroded from the uplifted basin margins. A series of large,deep lakes developed, resulting in the deposition of the lacustrine Kipper Shale. The Longtom unconformityseparates the freshwater lacustrine dominated Emperor Subgroup from fluvial/alluvial and marinesediments of the Golden Beach Subgroup. Many of the earlier generated faults were reactivated duringthis tectonic phase. Rift-related extensional tectonism continued until the early Eocene and producednorthwest-southeast-trending normal faults, especially in the Central Deep. A sequence of alluvialfluvial,deltaic and marine sediments were deposited across the basin forming the Halibut Subgroup ofthe Latrobe. In the middle Eocene, sea-floor spreading had ceased in the Tasman Sea and there was aperiod of basin sag, during which the offshore basin deepened but little faulting occurred. In the lateEocene, a compressional period began to affect the Gippsland Basin, initiating the formation of a seriesof northeast to east-northeast-trending anticlines. Compression and structural growth peaked in the mid-24 |

dle Miocene resulting in partial basin inversion. All the major fold structures at the top of the LatrobeGroup, which became the hosts for the large oil and gas accumulations, such as Barracouta, Tuna, Kingfish,Snapper and Halibut, are related to this tectonic episode. Tectonism continued to affect the basinduring the late Pliocene to Pleistocene, as documented by localised uplift. Uplift affected the Pliocenesection on the Barracouta, Snapper and Marlin anticlines, as well as around the township of Lakes Entrance.Ongoing tectonic activity continues in the basin as seismic events which occur along and aroundmajor basin bounding faults to the present day.Post-rift depositional architectures and settings became dominant in the Gippsland Basin from the earlyOligocene, with the deposition of the basal unit of the Seaspray Group viz. the Lakes Entrance Formation.Onlapping sediments from this formation provide the principal regional sealing unit across the basin.Subsequently, the deposition of the thick Gippsland Limestone provided the critical loading for the sourcerocks of the deeper Latrobe and Strzelecki groups.Stratigraphy of the Gippsland Basin is summarised in Figure 2.13. Major faults were indicated in Figure2.11.Figure 2.13: Gippsland Basin stratigraphy (after Power et al., 2001). The Lakes Entrance Formationforms the regional seal. There are a number of geosequestration opportunities in the LatrobeFormation.| 25

2.2.2 Data reviewAs might be expected from a mature hydrocarbon province, data from the Gippsland Basin is abundant.However, locating relevant sections and well logs is a significant undertaking. To construct a geologicalmodel for 2D seismic analysis, we focused on interpretation of seismic line G92A-3000 shown inFigure 2.14. The line is located along the shore in Gippsland Basin; the precise location is shown inFigure 2.15, where we also show for reference the location of seismic line GGS185B-17a used for illustrationof CO 2 storage in Figure 2.12. We chose to interpret seismic line G92A-3000 because ofavailability of previous interpretations by Moore and Wong (2002) and Power et al. (2001), as well asavailability of log data from the Kyarra-1 well located on the line. Figure 2.14: Seismic section G92A-3000, the basis of the conceptual 2D model. The coloured linescorrespond to interpreted faults.26 |

Figure 2.15: Location of seismic line G92A-3000 is shown in red. Seismic line GGS185B-17a used forillustration of CO 2 storage in Figure 2.12 is shown in yellow.| 27

0South-WestNorth-East2depth (km)46Figure 2.17: Seismic line GA2A-3000 with the indication of the part that was used for the constructionof the model.| 29

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3 Rock physics relationshipsNo extant remote sensing technique can directly measure the saturation of CO 2 . Appropriate rock physicsrelationships are therefore necessary to relate changes in geophysical measurements to changes in theCO 2 saturation. A wide variety of theoretical and empirical frameworks have been proposed to relatedensity, elastic-wave velocity properties and in situ electrical conductivity to the pore fluid and matrixproperties of rocks (e.g. Archie, 1942; Mavko et al., 2010; Zhdanov, 2008). A common feature of theseframeworks is that effective rock properties can be computed from a full suite of fundamental modelparameters such as the mixing between two member rocks or the formation water and CO 2 saturations.3.1 Elastic propertiesThe elastic properties of the subsurface change as CO 2 replaces formation water. The seismic modellingdone in this section uses P-wave velocity, V P , S-wave velocity, V S , and density, ρ, as input. We thereforehave to link changes in CO 2 saturation to changes in V P , V S and bulk density. We used two different rockphysics frameworks, viz., a sandstone-shale model and a pure sandstone model. The difference betweenthe two models is that sandstone-shale models use depth dependent trend curves and allow for shalecomponents in the layers to be considered.3.1.1 Sandstone-shale modelGassmann (1951) relates the bulk and shear moduli of a fluid saturated isotropic porous, monomineralicmedium to the bulk and shear moduli of the same medium in the drained case. The relations he derivedare increasingly used for reservoir monitoring when changes in fluid saturation of a reservoir are relatedto seismic velocities (e.g. Gunning and Glinsky, 2004; Kazemeini et al., 2010). The 1D seismic modellingemployed in this study (Section 4.1.3) is based on Delivery (Gunning and Glinsky, 2004), a tracelocal layer stack based Bayesian seismic inversion program. The rock physics framework discussed inthe following therefore corresponds to the one implemented in Delivery. In Delivery, each layer is modelledas a mixture of two finely-laminated end-member rock types; a permeable member such as sandor carbonate, and an impermeable member, such as shale or mudstone. The regional rock physics isimplemented by a set of trend curves.Trend curves for two member rocks are derived from logging information in wells drilled through intervalsdeemed to be representative of the rock behaviour in the region to be inverted. In the case of the SWHub project, the nearest well to the target region with well logging data available as of late 2011 is LakePreston 1. In the following, we will derive the necessary trend curves to model the seismic response as afunction of the mixing ratio between a permeable and non permeable member and the CO 2 respectivelyformation water saturation. Figure 3.1.a shows the in situ V P log, with the sand sections (permeablemember) highlighted in red. We use interval averages to perform the regressions. This is to prevent thesituation where intervals with a lot of data dominate the regression while only being representative for alimited depth range. Given the age (the Lake Preston 1 well was logged in 1973) of the well logging dataand the fact that the well suffers from washout in the shaley sections, we limited ourselves to deriving onepermeable and one impermeable member rock for the whole well instead of deriving individual memberrocks for each geological unit.| 31

a)0b)depth (m)100020003000400050000 2000 4000 6000 8000depth (m)1800 2000 2200 2400 2600c)derived porosity0.06 0.10 0.14 0.18o o o oo oooo o o ooo ooooooo oooooooooooo o o ooo oo oo oooooo ooo ooooooo o oo ooo oooooooooo ooo o oo oooo ooooooooooooooo oo oooo oooooooo oo oo oooooooooo ooooo oo oooooooo oooooooooo ooooo ooo ooooofitted trendfitted trend +/− residual standard errorfitted trend +/− two residual standard errors3800 4000 4200 4400 4600 4800in−situ vp (m/s)ooo o oooo oooooo ooo oo oo o o oo oooooooooo o o oooooo ooooooooooooo oooooooooooooooooooooo ooooo oooooooo oo oooooo o ooooooooooooooooooooo o ooooofitted trendoooooglobal trendoo oo o o oo o o ofitted trend +/− residual standard error oooo oofitted trend +/− two residual standard errorsooo3800 4000 4200 4400 4600 4800in−situ vp (m/s)in−situ vp(m/s)Figure 3.1: Illustration of derivation of rock physics relationships relating porosity to depth. Figure (a)shows the Lake Preston in situ V P versus depth log. Figure (b) shows the linear regressionfor the in situ V P versus depth using and Figure (c) shows the derived porosity versus the insitu V P . In Figures (b) and (c), black crosses show the mean and standard deviations for theinterval averages that were used in the regressions. To properly model the sand endmember,only data at depths indicated by the red markers in Figure (a) were used in the regressions.32 |

For the reservoir member a porosity versus depth curve is preferred to a bulk density versus depth trendcurve. Given a density log, porosity φ can be derived using (e.g. Sera, 1984)φ = ρ b − ρ gρ w − ρ g(3.1)where is ρ b is the bulk density, ρ g the grain density and ρ w the formation water density. Due to the lackof any shear wave data, a global average was used for the V P versus V S trend curve. For the permeablemember, we derived the following trend curves,φ = (0.74473 − 0.00015V P ) ± 0.01503V S = (−855.88 + 0.8042V P ) ± 121.92(3.2)V P = (3369.4 + 0.43671d) ± 53.802where φ is porosity, V P is P-wave velocity in m/s, V S is shear-wave velocity in m/s and d is depth in m.We note that sandstone velocity (V P ) is very fast. This is attributed to post-burial uplift and erosion, andis expected from inspection of the core. Figure 3.2 shows core from the Pinjarra 1 well with high levelsof cementation and compaction of the sandstone.Figure 3.2: Photograph of core from the Pinjarra 1 well. This particular core section is from a depth of2438 m. High degrees of compaction and cementation are evident and lead to higher thanexpected values of V P for sandstones for the SW Hub rock physics. A sample was takenfrom this core section for petrophysical analysis by another group. (Barclay et al., 2009)| 33

As a result of washouts in the shale sections of the Lake Preston 1 well, the density log could not be usedand a global average was used instead. There are no shear wave measurements. The rock physics for theimpermeable member is given by the following trend curves,ρ = (280.586V P 0.265 ) ± 70V S = (−858.93 + 0.7700V P ) ± 121.92V P = (1748.7 + 0.3394d) ± 182.98(3.3)where ρ is bulk density in kg/m 3 and other quantities were defined in Equation 3.2.Trends for both endmembers include uncertainties which are given by the standard errors of the linearregressions. These uncertainties are an integral part of modelling undertaken in Section 4.1.3 of thisreport. A similar approach to incorporating uncertanties in rock physics relationships was taken by Chenand Dickens (2009).Given the trend curves for the two member and the net-to-gross, we can compute the impedance changeas function of CO 2 saturation. Figure 3.3 shows the impedance change for the reservoir layer in theconceptual model developed in this work for the SW Hub Project. There is a nonlinear relationshipbetween impedance change and the CO 2 saturation and, echoing Figure 1.2, it is easier to detect changesof CO 2 saturation at small saturations than it is for larger saturations. Even so, determining an exactsaturation value from seismic alone will be difficult.00.0impedance change (%)−2−4−6−0.5impedance change gradient−80 10 20 30 40 50CO 2 saturation (%)−1.0Figure 3.3: Impedance change as a function CO 2 saturation in the potential reservoir area for the SWHub CCS Project. A 5% change in saturation will cause a larger change in accousticimpedance if the initial saturation is 5% compared to the situation where the initial saturationis 30%.34 |

3.1.2 Clean sandstone modelThe following workflow, based on the Gassmann fluid substitution, is used in Sections 4.1.4.2, 4.1.6.1and 4.1.4.3 of this report. This workflow computes elastic properties of a rock after formation water isdisplaced by CO 2 from the following properties• moduli of dry and formation water saturated rock (µ, K Dry , K Sat );• bulk moduli (incompressibilities) of formation water and CO 2 (K B , K CO2 );• porosity (φ);• CO 2 saturation (S); and• bulk modulus of the solid grain material (K G ).The formation water saturated rock moduli (µ and K Sat ) are estimated from bulk density (ρ) and V P andV S velocities taken from log data usingµ = V S 2 ρK Sat = V P 2 ρ − 4/3µ.(3.4)The dry bulk modulus (K Dry ) can be calculated from porosity, formation water modulus, and grainmodulus. The bulk modulus of the solid grain material can be calculated from the volume fractions ofthe minerals obtained from well logs or core samples. Then the moduli of the grain mixture can becomputed using the average of the upper and lower Hashin-Shtrikman bounds (Hashin and Shtrikman,1962; Mavko et al., 2010). However, if the interval of interest is dominated by relatively clean, quartzrich sandstone, the bulk modulus and density of the grain material are close to the values of quartz(K G = K Quartz = 36.6 GPa and ρ G = ρ Quartz = 2.65 g/cm 3 ) so that K Dry can be calculated from theGassmann equationK Dry = K Sat()φ K G − K BK B+ 1 − K Gφ K G − K BK B− 1In-situ bulk modulus (and density) of formation water and CO 2 are estimated from Lemmon et al. (2012).These properties depend on temperature and pressure, which are based on log data from wells in the areaof the interest. The bulk modulus of formation water/CO 2 mixture (K W ) is computed with mixing ruleof Wood (1955) (Mavko et al., 2010) :(3.5)1= 1 − S + S . (3.6)K W K B K CO2The use of Wood’s equation assumes uniform saturation; a reasonable assumption for sandstones atseismic frequencies (Johnson, 2001; Caspari et al., 2011). We assumed that the saturation is given by50% formation water, 50% gas mixture, which is the approximate maximum residual CO 2 saturation forsandstones (Benson et al., 2012; Ivanova et al., 2012). Figure 3.3 showed that the effect on modellingresults of saturations between 30 and 50% is not critical.| 35

Finally, the Gassmann equation (3.5) can be used to calculate the bulk modulus of the rock after injection(that is, saturated with a mixture of formation water and CO 2 ) by solving the equation for K Sat and bysubstituting K W for K B :K CO2 Sat = K Dry()φ K G − K WK W− 1φ K G − K WK W+ 1+ K G. (3.7)The density of the gas-saturated rock is then used to compute the P and S-wave velocities after injection.36 |

3.2 Electrical propertiesThe model used for EM studies was based on that derived from seismic studies, and Equation 3.2 wasused to derive layer resistivity using Archie’s law (Archie, 1942), one form of which isρ = aφ m ρ wS wn (3.8)where a is tortuosity, φ is porosity, ρ w is formation water resistivity, S w is formation water saturationand m and n are constants with 1.8 ≤ m ≤ 2 and n ≈ 2. Mabrouk et al. (2012) suggest methods ofcalibration of a and m. Because the relationship in Equation 3.8 is known (Vinegar and Waxman, 1984) tobe problematic in zones of moderate-to-high shale, such as in the upper Lesueur formation, an alternativeeffective medium model relating bulk resistivity to individual fractions might be more appropriate. Suchmodels are discussed in Section 3.3. Kennedy and Herrick (2012) give an excellent tutorial of the generalapplicability of Archie’s law.3.3 Effective medium theoriesEffective medium theories describe the macroroscopic properties of a medium in terms of the propertiesand fraction of that medium’s components. We distinguish between theories that are valid in thelong-wavelength limit, and ‘dynamic-equivalent’ theories which replace a heterogeneous medium withhomogeneous one at arbitrary frequencies. The simplest of these theories (e.g. Archie, 1942) describetwo-phase materials but more modern theories (Habashy and Abubakar, 2007; Zhdanov, 2008), describemulti-phase materials. Modern effective medium theories have their genesis in work by Stroud (1975).To illustrate why effective medium theories are preferred over Archie’s law (for example), we considerthe Zhdanov (2008) model of complex electrical resistivity (Generalised Effective Medium Theory ofInduced Polarization or GEMTIP). The general form of this model in Figure 3.4(a) treats heterogeneitieswith arbitrary shapes, but when heterogeneities are spherical, as in Figure 3.4(b), this model closelyapproximates a Cole-Cole (Cole and Cole, 1941) model of complex resistivity. Esteban et al. (2011)demonstrate the utility of the Zhdanov (2008) model in modelling pyrite inclusions in petroleum explorationcores.Zhdanov (2008)’s model for a N-phase medium’s effective resistivity ρ e isρ e = ρ 0{1 +N∑l=1[f l M l[1 −11 + (iωτ l ) C l]] } −1(3.9)where ρ 0 is the matrix resistivity, f l is the fraction of the l-th phase, C l is the relaxation parameter of thel-th phase, ω is the angular frequency (2πf) and i is √ −1 and the quantities M l and the time constant τ lare definedM l = 3 ρ 0 − ρ lρ 0 + 2ρ l| 37

] −Cl2α lwhere a l , α l and ρ l are the size, surface polarisability coefficient and resistivity respectively of the l-thphase.Equation 3.9 is plotted in Figure 3.5 for a SW Hub-like environment over a range of frequencies representativeof a crosshole inductive EM system. The amount of formation water in the medium is variedfrom 1 to 5%. At lower frequencies, effective resistivity asymptotes to matrix resistivity. However, atmoderate to high frequencies, effective resistivity shows significant nonlinear behaviour.Figure 3.5 is appropriate for use in time-lapse simulations where reduction in the percentage of formationwater corresponds to an increase in CO 2 saturation. Electromagnetic response at low frequencies issensitive only to the matrix resistivity. At high frequencies, the complex behaviour of ρ e requires aneffective medium theory to correctly interpret resistivity. Without such a theory, resistivity recoveredfrom electromagnetic measurements will suggest an unrealistically-low CO 2 saturation.38 |

300Lesueur project GEMTIP Rock physics A AbsResistivity m290280270260 Brine123452501 5 10 50 100 500 1000 B Arg0.20Phase Radians0.150.100.050.001 5 10 50 100 500 1000Frequency HzFigure 3.5: Illustration of the dependence of effective resistivity on formation water percentage inGEMTIP as applied to SW Hub rock physics. By implication, CO 2 is included in the formationwater. The complex frequency-dependent nature of resistivity is clearly evident. Forexample, only at low frequencies does the effective resistivity approximate matrix resistivity.At higher frequencies, similar to those used in crosshole EM surveys, effective resistivitycan be 30% lower than matrix resistivity and accompanied by a phase anomaly. This in turn,complicates interpretation of resistivity distribution.| 39

3.4 SummaryRock physics relationships are required in order to relate changes in geophysical measurements tochanges in the CO 2 saturation. These relationships are used to build the models that will be used inthe remainder of this report. The chief limitation of these models is the data upon which they are based.For the SW Hub, there is a mixture between vintage well logging and modern seismic data. This willbe addressed by data collected during January-February, 2012 from the Harvey-1 well (Figure 2.2). Forour CarbonNet conceptual model, the data and well are some distance from the site indicated by Aldous(2010). For CarbonNet conceptual models in this report to apply directly to the project, geological conditionsmust be reasonably consistent over the nearly 35 km between the model and data sites (Figure 2.15).Mostly, for sedimentary basins, this is a reasonable assumption. However, as the lines GA2011-LL1 andLL2 (Figure 2.8) at SW Hub show, this is not necessarily the case.40 |

4 Geophysical remote sensing techniquesThere are two distinctively different approaches to analyse the information contained in a geophysicalsurvey.• Qualitative interpretation: Also known as either geophysical imaging or data mapping, the aim isto image the geophysical property that is directly measured by a given geophysical remote sensingtechnique. Changes in the images are then qualitatively related to changes in the CO 2 distribution.An example for a qualitative interpretation is the section on the time lapse seismic in 2D (Section4.1.4) we are able to make qualitative statements about the extent of the zone that is saturated withCO 2 . It is however not possible to assess the uncertainties of any derived extents of the zone withCO 2 or a saturation value; and• Quantitative interpretation: The goal is to find the distribution of models that are in agreementwith the prior information and the data. The focus is less on the analysis of the recorded data andmore on finding models that explain the data. Given an appropriate rock physics framework theaim is to invert directly for the saturation of CO 2 . The advantages of this approach are illustratedin section 4.1.3 where we are able to estimate the probability to detect CO 2 .Delivery (Gunning and Glinsky, 2004) is an example of a software package that allows the quantitativeinterpretation of seismic data. While originally developed for the hydrocarbon exploration, it is applicableto geosequestration and should be seen as the kind of tool necessary to enable the quantitativeinterpretation of e.g. gravity and EM data.Section 4 forms the bulk of this report. Geophysical measurement techniques viz. seismics, EM andgravity are modelled using various dimensional approximations. The models were defined in Section 2and are populated using rock physics relationships in Section 3. Other techniques, NMR and microseismics,were not modelled. Lacking modelling capability, both techniques are covered by literaturesurveys.| 41

4.1 Reflection seismic surveyingSeismic waves are mechanical perturbations that travel through the Earth at a speed which is governedby the acoustic impedance of the medium in which they are travelling. The acoustic impedance (Z), isdefined Z − Vρ, where V is velocity and ρ is the rock’s bulk density. When a seismic wave travellingthrough the Earth encounters an interface between two materials with different acoustic impedances,some of the wave energy is reflected from the interface and some is refracted through the interface. Atits most basic level, the seismic reflection technique consists of generating seismic waves and measuringthe time taken for these waves to travel from the source, reflect off an interface to detection by an arrayof receivers (or geophones) at the surface.Seismic data form a critical role in characterising structure. Once structure is characterised, it can thenbe used to constrain interpretations of other geophysical results (e.g. EM and gravity). The interpretationof seismic data requires an understanding of the noise present in the image.4.1.1 Seismic noiseIn order to assess detectability of CO 2 , we need to understand noise levels in recorded data: in noise-freedata we would be able to detect minute changes , however very noisy data can obscure even large changesin the subsurface caused by CO 2 . One way of quantifying the amount of noise in the data is to use thefollowing S/N expression from Hatton et al. (1986):(S/N) i =√[gi,i+1 ] MAX1 − [g i,i+1 ] MAX. (4.1)In the above equation, [g i,i+1 ] MAXis the maximum of normalised cross-correlation between the twoneighbouring traces with indices i and i + 1 in the given time window.To establish what levels of noise we can expect in data acquired to monitor CO 2 in different conditions,we evaluate the S/N ratio in the real data sets; we have applied expression (4.1) to seismic lines11GA_LL2 (Fig. 4.1) and GA92A-3000 (Fig. 4.24). The length of the time window was 100 ms. TheS/N values were later used to generate realistic amounts of noise in the synthetic data used to model CO 2detectability.Using the S/N attribute, we can try to explain any variations in this attribute in the data. For example,as shown in Figure 4.1, there is a "mute zone" of low S/N ratio on the west part of the seismic section11GA_LL2. To understand this zone, we perform the following analysis of surface related attributes.4.1.1.1 Surface related noise analysisTo understand the nature of the S/N in the data, we can look at quality control (QC) attributes on theseismic line 11GA_LL2. To be certain that the processing does not affect these attributes, we have alsoperformed basic processing of the raw data. Processing steps are listed in Appendix B. The QC wasperformed on both the shot and receiver gathers to distinguish between the different coupling of thesource and receivers. To measure noise, signal and ground-roll levels in the data, we need to split theshot and receiver gathers into separate windows, as illustrated in Figure 4.2. Using these windows, wecomputed the following QC attributes:42 |

0500100021.81.61.4 T (ms) 150020001.210.8250030000.60.40.24200 4400 4600 4800 5000 5200CDP0Figure 4.1: log 10 scale of S/N ratio for seismic line 11GA_LL2. The horizontal axis is oriented East-West with increasing CDP number that is related to the geometry of the seismic acquisition. x TFigure 4.2: Schematic of a shot/receiver gather showing the definition of windows for attribute-basednoise analysis. The horizontal axis represents shot-receiver offset, the vertical axis is thetravel time. The red line corresponds to direct arrivals, the green cures represent reflectedsignals, and the orange line represents the ground roll.| 43

• ratio of the mean absolute amplitudes in ’Signal’ and ’Noise’ windows, A S /A N ;• mean absolute amplitudes in ’Ground roll’ window; and• centroid frequency in ’Signal’ window.We plotted these attributes versus the offset (station number), as shown in Figure 4.3. These plots indicatedthat there is a big increase in the A S /A N attribute for common receiver gathers while for commonsource gathers, this attribute remains relatively unchanged. This indicates a possible change of groundconditions along the lines. Moreover, there is a strong correlation of the increased A S /A N ratio in thecommon receiver gathers with the ’mute’ zone of the data corresponding to the Lesueur formation, asshown in Figure 4.4.To investigate the potential change in the surface conditions along the seismic lines, we overlaid thelocation of the lines and the location of the change in the A S /A N attribute on an aerial image of the area.As can be seen in Figure 4.5, the change in the attribute correlates with the visible change of the surfaceon the aerial image.This analysis indicates that any reflectors in the ’mute’ zone are hidden by the noise and that they might berecoverable by using specialised processing techniques, such as common reflection surface (Eisenberg–Klein et al., 2008) or multi-focusing methods. Moreover, the low S/N ratio related to the surface conditionscan be mitigated by utilising some form of buried source and/or receivers monitoring method, aswe discuss in Section 4.1.6.1.44 |

108A S/A N64202000 2100 2200 2300 2400 2500 26003 x 109ground roll2102000 2100 2200 2300 2400 2500 2600Freq. (Hz)504030SOUSRF202000 2100 2200 2300 2400 2500 2600StationsFigure 4.3: The three QC attributes computed using source (red) and receiver gathers (blue): ratioA S /A N of the mean absolute amplitudes in ’Signal’ and ’Noise’ windows, mean absoluteamplitudes in ’Ground roll’ window, and centroid frequency in ’Signal’ window.| 45

108A S/A N64202000 2100 2200 2300 2400 2500 260003 x 109500 T (ms) ground roll1000 21500 1200002000 2100 2200 2300 2400 2500 26002500Freq. (Hz)5030004030SOUSRF2100 2200 2300 2400 2500 2600stationFigure 4.4: A S /A N QC attribute along the second seismic line and the corresponding data. The horizontalaxis 20 is oriented East-West with increasing station number that is related to the geometryof the2000 seismic acquisition. 2100 2200 2300 2400 2500 2600Stations46 |

Figure 4.5: The seismic lines superimposed on the areal image of the area. The red boxes show approximatelocations of the change in A S /A N attribute along the seismic sections. These locationsseem to be correlated with the changes in the surface character.| 47

4.1.1.2 Quantitative noise analysisSeismic noise is commonly expressed in terms of a signal to noise ratio. It is important to keep in mind,that a signal to noise ratio is only a relative noise measurement. This means two surveys with exactlythe same absolute amount of noise but different impedance contrasts across layer boundaries will havedifferent signal to noise ratios, while being of the same quality. This point is illustrated in Figure 4.6,where we show a numerically-modelled (Fomel et al., 2006; Fomel, 2011) seismogram across a singleinterface. The same amount of absolute noise is added to the waveforms. The blue waveform encountersa higher impedance contrast and hence the amplitude associated with the reflection is larger. Clearly theblue waveform has a higher signal to noise ratio than the red waveform. However, this does not mean theblue waveform is of higher quality from a signal processing point of view, as the same amount of noisehas been added to the two waveforms.5e−072.0 km/ssignal3.0 km/samplitude0first layernoisesecond layer2.0 km/s3.5 km/s−5e−070.3 0.4 0.5 0.6time (s)Figure 4.6: Illustration of the failings of common S/N interpretations. The figure compares twonumerically-modelled traces for an interface with two different velocities in the second layerand the same level of noise added. The acoustic impedance contrast is larger for the bluetrace than for the red trace. Clearly, the two signals have a different signal to noise ratio yetboth have the same amount of noise added.Since they preserve the relative amplitude between reflectors, true-amplitude migrated data are necessaryto derive absolute measurements of noise. However, these data are not a product of the standard workflowsapplied to seismic data. Indeed, compared with marine seismic data, production of true-amplitudemigrated sections for land data is fraught with difficulty because of the much higher noise levels on land.Given a well, and true-amplitude migrated data, Delivery can be used to obtain robust estimates of noise.Appendix C contains a detailed description of a workflow to produce true-amplitude migrated seismicdata.48 |

4.1.2 Data processingThe end goal of collecting seismic data is to characterise the Earth’s structure. By applying differentalgorithms to the data, seismic images are produced that are fit for particular purposes. For example, theimages in Figure 2.8 are the product of a processing workflow designed to optimise structural interpretation.For stratigraphic and quantitative interpretation, it is desirable to preserve reflector’s relative amplitude.Such processing is known as true- (or relative-) amplitude (Schleicher et al., 1993) processing. A workflowto produce such sections from land seismic data is described in Appendix C. It is not unreasonableto expect similar requirements for seismic data collected for quantitative interpretation of CO 2 plumes.The processing of seismic data acquired onshore to be used for amplitude versus offset (AVO) or quantitativeinterpretation (QI) purposes is particularly problematic in the Australian environment where tertiarycarbonates are thick, and in particular, where the they may be exposed at surface. Particular steps arerequired in this environment to remove linear noise trains and reverberations caused by the near surfacecarbonates. Performing this processing, and still achieving a seismic section where the reflectionstrength is proportional to the actual change of acoustic impedance of the reflecting boundary, is particularlydifficult. In general, there is no single perfect processing flow that achieves this in all situations.However, a few generalisations can be made, and checks performed, that will allow us to get close totrue-amplitude processing. Such a workflow is illustrated in Figure 4.7, and described in much greaterdetail in Appendix C.In any modern multichannel processing flow, the key stages of determining the correct velocity, mutes,and surface statics, are vital in providing an image that can at least be interpreted. It is always suggestedthat a non-true amplitude product be provided in conjunction with the true-amplitude product to showthe effects of processing decisions that may compromise S/N, but benefit true-amplitude processing, beeasily measured and compared. On 2D data, there is no way of handling out-of-plane reflections, and itcan be shown that a false AVO response can be generated just from surface structure on a synthetic dataset. Therefore, it is always recommended that only 3D data be used for quantitative interpretation.| 49

seismic input dataSPS, RPS, XPSand uphole datauphole survey dataand/or well dataspike removalmono frequencyremovalnavigation mergefold plotLMO QC − firstbreak picksnear surfacevelocity modelgain recoverydeconvolutionnon productionvelocityversion 1refraction model buildrefraction static stackelevation static stackcomparecomparecomparedeconvolutionSurface consistent amplitude compensationlinear noise removalstackresidual staticsiteration loopdemultiplevelocity repickremoval inital gain recoverydata regularisation AGC migrate version 1migrate version 2Q and gain recoveryvelocity repickresidual gain recoverystack and AVOiteration loopwell calibrationQ amplitude correctionresidual NMOFigure 4.7: Schematic illustration of the workflow required to produce true-amplitude seismic sections.The abbreviations SPS, RPS and XPS are from the Shell standard (Shell InternationalePetroleum Maatschappij, B.V, 1995) for land navigational data and respectively, are ShotPositioning, Receiver Positioning and a link indicating which receivers were used in whichchannel for each shot. This workflow is described in greater detail in Appendix C.50 |

4.1.3 1D Reflection seismicsSeismic monitoring of CO 2 focuses commonly on detecting differences between different vintages ofseismic and then relating those changes qualitatively to changes in CO 2 saturation in the reservoir. Giventrue amplitude migrated seismic data a model based inversion will allow for quantitative interpretationof the seismic data and provide a much more complete picture of the subsurface. If the model basedinversion is performed in a Bayesian framework, uncertainties in rock physics, structure (layer boundaries)and lithology (net-to-gross) can all be taken into account. Bayesian approaches rely on efficientlysolving the forward problem. This means that in the case of reflection seismic they are currently limitedto 1D approximations. Gunning and Glinsky (2004) introduce Delivery, a model-based Bayesian seismicinversion code that will be used below to analyse the detectability of CO 2 for the SW Hub project. Deliveryembeds rock physics models in the prior distribution and thus allows us to directly invert for thesaturation of CO 2 given true amplitude migrated seismic data.Barclay et al. (2009) identified structures to east of the anticline shown in Figure 2.6 as a potentialstorage area for CO 2 in the SW Hub project. This corresponds to the section at about 10 km horizontaldistance in the conceptual model shown in Figure 2.9. We constructed a 1D conceptual model for thislocation for Delivery by blocking the log data for the Lake Preston 1 well and stretching and squeezingthe blocked model so that the formation boundaries match the ones in the conceptual model. We assumethat the CO 2 is uniformly distributed over a reservoir layer near the top of the Wonnerup. Preliminarywork in the region (Barclay et al., 2009; Varma et al., 2009) identified residual trapping as the primarytrapping mechanism, which is more likely to achieve more contact with the reservoir pore space becauseof the lower permeability and low strata correlation distances in the Yalgorup when compared to a highpermeabilityreservoir than the situation where a plume migrates upwards to a regional seal.Figure 4.8 shows the conceptual model and the seismic signal for a situation with no CO 2 and 30% CO 2saturation. The impedance change in the reservoir layer due to the replacement of formation water byCO 2 has two effects on the seismic signal. Firstly, the amplitude of the reflections at the top and base ofthe reservoir is changed and secondly, reflectors below the reservoir experience a time shift.Figure 4.9.a shows the probability for 30% CO 2 and pure formation water as function of the reflectioncoefficient across the top boundary of the reservoir layer. We recall that the derived trend curves insection 3.1.1 include uncertainties. The distribution of the reflection coefficients for the two cases isdue to the uncertainties in the derived trend curves. We also note that the two distributions overlap eachother. This means that for the range of reflections coefficients between −5% and 7.5% there is always acertain probability for the 30% CO 2 saturation and the pure formation water model. Figure 4.9.b showsthe probability for 30% CO 2 as function of the reflection coefficient. As we add additional noise we seethat the distributions become wider and it becomes more difficult to distinguish between the two cases.We perform a single vintage computational experiment to demonstrate how the rock physics, structuraland lithological uncertainties influence the seismic detectability of CO 2 . The uncertainties we use arebased on a Bayesian inversion for the structure and lithology using a synthetic signal as the observeddata. The magnitude of uncertainty on the structure and lithology is therefore what one would face in apractical setting after a high quality seismic data has been collected and processed. Figure 4.10.a shows| 51

a)0b)0123depth (m)10002000depth (m)1000200054679 8 1011121314151617MyallupWonnerup reservoir1.00.90.80.7net to gross0.630003000180.5Figure 4.8: a) Synthetic 1D seismic with only formation water (blue) and a 30% saturation of CO 2 (red)in the reservoir layer. b) 1D conceptual model with layer boundaries in green and layercoloured according to their net-to-gross values. Numbers 1 to 18 in (b) refer to the blockedlayer model derived from well logging data.a) b)probability (%)3020102.5 % noise added5 % noise added8030 % CO 2Brine7060.8 %0−10 −5 0 5 10probability CO 2 (%)1009060504030201030 % CO 22.5 % noise added5 % noise addedBrine0−10 −5 0 5 10reflection coefficient (%)reflection coefficient (%)Figure 4.9: a) Probability of formation water and a 30% saturation of CO 2 as a function of the reflectioncoefficient across the top boundary of the reservoir layer (layer 15 in Figure 4.8. b)Probability of a 30% CO 2 saturation as a function of the reflection coefficient across the topboundary of the reservoir layer.52 |

the detectability of CO 2 when only rock physics uncertainties are taken into account. The probabilitiesfor CO 2 are computed using Delivery and based on the full misfit between the two seismograms. Theprobability of CO 2 taking rock physics uncertainties into account and an additional noise of 2.5% is 61%based on the top reflector alone, but the full misfit boosts this to 66%.When we allow for structural uncertainties, the detectability is reduced in particular for higher noiselevels. Changes in the amplitude of reflectors due to CO 2 replacing formation water can now be explainedby rearranging the layers and hence by interference between wavelets associated with layer boundaries.When lithological (net-to-gross) uncertainties are taken into account, the detectability decreases even forlow noise levels, an impedance change in layer due to the presence of CO 2 might now be explained inthe inversion by a shift in the net-to-gross value. Clearly when rock physics, structural and lithologicaluncertainties are taken into account, CO 2 is no longer sufficiently detectable using only one vintage. Thisconfirms the well known fact that seismic monitoring of CO 2 is only possible with time lapse seismic.a) rock physics uncertaintiesb) rock physics and structural uncertainties60 80 100probability (%)508060 80 100probability (%)508040 30 20 10CO 2 saturation (%)01 2 3 4 5 6 7noise (% RFC)706050probability (%)40 30 20 10CO 2 saturation (%)01 2 3 4 5 6 7noise (% RFC)706050probability (%)c) rock physics and lithological uncertaintiesd) rock physics, structural and lithological uncertainties60 80 100probability (%)5040 30 20 10CO 2 saturation (%)01 2 3 4 5 6 7noise (% RFC)80706050probability (%)60 80 100probability (%)5040 30 20 10CO 2 saturation (%)01 2 3 4 5 6 7noise (% RFC)Figure 4.10: Probability for the detection of CO 2 in the reservoir layer in the Wonnerup (layer 15 inFigure 4.8) as a function of noise and saturation in a single vintage experiment.80706050probability (%)Given time lapse seismic data, Delivery allows us to directly invert for the change in CO 2 saturation.Uncertainties in rock physics, structure and lithology no longer affect the detectability. However, nonrepeatablenoise in the seismic imaging experiment, or noise that results from time-dependent processescan be a serious issue and reduce the practical detectability (e.g. Calvert, 2005). We neglect such effectsin the time lapse experiment. Figure 4.11 shows the probability to detect CO 2 in the second vintage,when the base survey (first vintage) contains pure formation water and the goal of the model-basedinversion is to estimate the unknown CO 2 saturation in the second vintage. We note the much-improveddetectability of CO 2 in a time lapse setting. It is unlikely that two vintages of seismic will have the samequality. It is nearly impossible to achieve the same coupling for the sources and to some degree for the| 53

a) b)100probability (%)80602% RFC4% RFC6% RFC0 10 20 30 40 50CO 2 saturation (%)60 80 100probability (%)5040 30 20 10CO 2 saturation (%)01 2 3 4 5 6 7noise (% RFC)Figure 4.11: Probability for the detection of CO 2 in the reservoir layer in the Wonnerup (layer 15 inFigure 4.8) using time lapse seismic where vintage one is CO 2 free and vintage two containsan unknown saturation of CO 2 . Figure 4.11a highlights the change in CO 2 detectability forparticular noise levels. The probability of detecting CO 2 at a saturation of 30% decreasessignificantly for noise levels higher than 2% RFC.1009080706050probability (%)receivers between two surveys. If a differentiation of the wave fields is used to process the time lapsedata, the better quality data has to be reduced to the quality of the poorer quality data. In contrast to this,the model based inversion implemented in Delivery allows the use of datasets with different qualities.However even with time lapse seismic determining the saturation value is difficult due to the flatteningout of the impedance curve once the saturation of CO 2 is above 20% (see Figure 3.3).The true value of this workflow is that it allows the estimation of minimum S/N ratios required foradequate detection of a CO 2 plume within the assumptions that are required to be satisfied for 1D layerbasedmodels to be valid. These ratios are important for survey planning, processing workflow designand repeatability targets. From Figure 4.11, we can conclude that achieving a confidence level of atleast 95% to detect a 30% CO 2 saturation, the maximum allowed noise is about 4% on the reflectioncoefficient scale. Assuming that the reference event across the top of the Wonnerup is about 14% theS/N ratio has to be about 3.5 or better to achieve the desired level of detectability.54 |

4.1.4 2D Reflection seismicsModelling the 2D seismic response of the earth is a useful compromise between gross 1D approximationsand computationally intensive 3D models. Modelling in this section is based upon the conceptual modelsin Sections 2.1 and 2.2 and uses their respective rock physics relationships.4.1.4.1 SW Hub Line GA2011-LL1Using the 2D conceptual model in Figure 2.9, the derived rock physics and one geostatistical realisationof the net to gross distributions in the structural units we can derive a 2D velocity and density field.Figure 4.12(a) shows the baseline velocity model, the change in velocity due to CO 2 saturation of 30%is shown in Figure 4.12(b). We use Madagascar (Fomel et al., 2006; Fomel, 2011), an open sourcesoftware package, to perform the finite-difference modelling to illustrate the sensitivity of 2D time lapseseismic with respect to a CO 2 reservoir. The survey parameters are given in Table 4.1 and the in-linedistance corresponds to the horizontal distance in the 2D conceptual model (Figures 2.9 and 4.12(a)).Table 4.1: Survey parameters used for finite-difference modelling of seismic responses for the SW Hubmodel. Shot spacing is twice that used when collecting the 2011 survey.ParameterValue (km)first receiver in line location 0.0last receiver in line location 16.0receiver depth 0.01receiver spacing 0.05first in line shot location 1.0last in line shot location 15.0shot spacing 0.05shot depth 0.01receiver spread 2.0Each shot gather was simulated using 2D finite difference modelling of the acoustic wave equation inthe time-domain. Random noise is added to each shot and the shot gathers are muted before formingcommon midpoint gathers. For each common midpoint gather, the results of a velocity analysis are usedas stacking velocities and Kirchhoff post-stack migration is applied to the normal move out stack imageto obtain the final image.The seismic image for the base line survey is shown in Figure 4.12(c) and Figure 4.12(d) shows theseismic image after the introduction of a 30% saturated CO 2 reservoir. The introduction of the CO 2causes changes in two reflectors to appear in the seismic image. One of these reflectors is associatedwith the top of the reservoir and the other with the base.Figure 4.13 shows time lapse seismic between different CO 2 saturations in the reservoir. The flatteningof the change in seismic impedance as a function of CO 2 saturation is illustrated in Figure 3.3. As aconsequence, the difference in the time lapse seismic is much more distinct for saturation changes at lowsaturations (Figure 4.14(c)) than it is for saturation changes at higher saturations (Figure 4.14(d)).| 55

Differences in time lapse seismic images are caused by two effects viz. shifts of reflector positions intime due to velocity chances and changes in amplitude due to changes in the seismic reflectivity. One ofthe challenges in time lapse seismic is to isolate changes in the reservoir from changes in the surroundingarea. Cross equalisation (e.g. Rickett and Lumley, 2001) and more recently image registration using alocal similarity attribute (Fomel and Jin, 2009) have been successfully applied to estimate and removetime shifts. Figure 4.14 shows the semblance cubes for the time lapse results shown in Figure 4.13 usingthe local similarity attribute (Fomel, 2007). End faces of each volume show sections indicated by bluegraticules and numbers. For example, the top surface of each volume shows semblance between differencedata vintages at roughly 1500 m depth. Comparison between Figures 4.14(a)–4.14(d) shows thatlarge differences are seen, particularly in the vicinity of the reservoir, at low saturations. At higher saturations,semblance between the two vintages shows smaller differences suggesting the loss of effectivenessin 4D seismics at higher saturations, as we would expect based on the computational experiments in theprevious section. In a practical situation the next step would be to extract the stretch factor that achievesmaximum semblance and then recompute the time lapse difference images using the appropriate stretchfactor (Fomel and Jin, 2009).56 |

(a) Baseline velocity model(b) Velocity difference between baseline and 30% CO 2 saturated models(c) Baseline Kirchoff post-stack migrated seismic section(d) 30% CO 2 saturated Kirchoff post-stack migrated seismic sectionFigure 4.12: Results of modelling time-lapse seismic responses. Baseline is taken to be prior to CO 2injection. The effect of a 30% CO 2 saturated reservoir on the velocity model is seen inFigure 4.12(b). Figures 4.12(c) and 4.12(d) illustrate the effect of CO 2 injection on theseismic response. Correlation between the velocity anomaly (Figure 4.12(b)) and the seismicanomaly (Figure 4.12(d)) is evident.| 57

(a) Difference between baseline and 2.5% CO 2 saturated reservoir(b) Difference between 2.5% and 5% CO 2 saturated reservoir(c) Difference between 5% and 10% CO 2 saturated reservoir(d) Difference between 25% and 30% CO 2 saturated reservoirFigure 4.13: Seismic time-lapse modelling of CO 2 injection. Baseline in taken as prior to CO 2 injection.All figures show differences in Kirchoff post-stack seismic responses. Larger differencesin seismic response are seen at lower saturations than at higher saturations. Moreover, themajor region of difference at higher saturations in Figure 4.14(d) is not seen over the reservoir.This suggests that time-lapse seismics loose effectiveness at higher CO 2 saturations,and therefore, as injection proceeds.58 |

(a) Baseline and 2.5% CO 2 saturation(b) 2.5% and 5% CO 2 saturation(c) 5% and 10% CO 2 saturation(d) 25% and 30% CO 2 saturationFigure 4.14: Semblance analysis of time-lapse seismic modelling. The colour scale runs from red toblue with reds indicating small differences between the two datasets and blues indicatinglarge differences. At lower CO 2 saturations, large differences are seen around the reservoir.At a small difference in CO 2 saturation at high saturations, little difference is seen betweenthe two data sets. The end faces on each volume show semblance at a particular section asdiscussed in the text.| 59

4.1.4.2 SW Hub Line GA2011-LL2As discussed in Section 2.1.3, we based the model on interpretation of seismic line 11GA_LL2 shownin Figure 2.8(a). The final model is shown in Figure 2.10, with the corresponding interval velocitiessummarised in Table 1. The P-wave velocities are well correlated with log data from Lake Preston 1well. The densities of the interval were obtained using Gardner’s empirical relation (Gardner et al.,1974). Since we do not have direct measurements of S-velocities, S-wave velocities were derived usingequation of Castagna et al. (1985) (mudrock line), V S = 0.86 · V P − 1.17km/s. The effective porosityfor the depth 2500m is 11%, which is based on the log data from well Lake Preston 1, the closest wellwith logs to the area of the interest.In-situ formation water and CO 2 properties are taken from Lemmon et al. (2012). The temperature wascalculated based on the temperature gradient in the area 2.1°C/100m and the surface temperature of18°C. The pore pressure was computed from the pressure gradient of 9.8kPa/m (hydrostatic pressure ofwater with salinity of 30,000 ppm, which has density of 999.850kg/m 3 ). In summary, the quantitiesneeded for the fluid property estimation are:• Pore pressure: 24.5MPa; and• Temperature: 70°CThe CO 2 and formation water properties at these conditions are:• V P = 441.82m/s ;• K CO2 = 0.143GPa ;• ρ CO2 = 0.731g/cm 3 ;• K Brine = 2.502GPa ; and• ρ Brine = 0.999g/cm 3 .We perform the Gassmann fluid substitution, as discussed in Section 3.1.2 using interval P-velocities asinput. The P-velocities were obtained as an average of velocities from borehole measurements in thearea. Since we do not have measurements of S-velocities, we use Castagna’s equation (mudrock line) fortheir estimations. For the same modelled injection interval, this gives:Pre-injection: 100% formation water,• V P = 4333m/s,• V S = 2556m/s,• ρ = 2.512g/cm 3 .Post-injection: 50% formation water, 50% gas mixture, which is the approximate residual CO 2 saturationfor sandstones (Ivanova et al., 2012; Benson et al., 2012).• V P = 4225m/s,• V S = 2564m/s,60 |

• ρ = 2.497g/cm 3 .This gives the change of acoustic impedance (AI) for P-waves due to injection of ∆Z = −335 ·10 3 (m/s) · (kg/m 3 ), which is approximately -3% change from the pre-injection impedance. The reflectioncoefficient caused by this impedance change for normally incident waves is approximately 0.016,which implies that only 0.026% of the seismic energy reflects from the plume. This low reflection coefficientis mostly caused by the low effective porosity of the reservoir.We modelled several CO 2 plume sizes and distributions. These were based on 2k, 5k, 10k, 20k, 40k,and 80k tonnes of injected CO 2 . The respective volumes of the CO 2 saturated rock are calculated fromthe pore pressure, temperature, effective porosity and saturation at the injection interval. The values forthe first four quantities are taken from well logs, the saturation is assumed to be 50%. We consider acylindrical shape of the plume. The diameter and thickness of the plume are constrained by the followingconsiderations. We expect that a plume with the diameter larger than the first Fresnel zone and thicknesslarger than the tuning thickness should be detectable; for this reasons, we consider plumes with diameterssmaller than the first Fresnel zone and the thicknesses smaller than the tuning thickness. Also, it isreasonable to expect the thickness of the plume to increase with its volume. For these reasons, wechoose the diameter and thickness of the five different volumes to be distributed along a line with slopegiven by the quotient of the first Fresnel zone and the tuning thickness. For the injection interval the porepressure is 24.5MPa, temperature 70°C, effective porosity 11%, saturation of 50%, and the respectiverock volumes of the CO 2 plumes with the dimensions of the six volumes are shown on a curve given inFigure 4.15 are summarised in Table 4.2.Table 4.2: Dimensions of the modelled CO 2 plumes for GA2011-LL2.Injected CO 2 (’000 tonnes) 2 5 10 20 40 80Rock volume (10 3 m 3 ) 49.75 124.36 248.73 497.45 994.90 1989.80Plume thickness (m) 3.62 4.92 6.19 7.80 9.83 12.39Plume diameter (m) 132.24 179.48 226.13 284.91 358.97 452.27All the plumes were distributed in one model for two reasons. First, the simultaneous placement ofthe different plumes requires computing only one forward model, which significantly saves on the timeneeded to generate the data. Second, the placement of the plumes in one section allows for easier comparisonof detectability of the different CO 2 volumes. The final model including the six CO 2 plumes isshown in Figure 4.16.To estimate the volume of CO 2 plumes that are detectable by seismic methods, we need to be able notonly to simulate the response of the plumes in seismic section, as we we discuss above, but also weneed to estimate the amount of noise in the real data. The seismic response of the plumes without anynoise added is shown in Figure 4.17, where we plot the migrated sections corresponding to the baseline,monitor, and difference. Clearly, without any noise we can detect all the plume sizes.To add realistic amount of noise to the synthetic data, we estimate the amount of noise in the real databy using expression (4.1) on seismic line 11GA_LL2, as discussed in Section 4.1.1. Then we add to the| 61

8007006002.00kt5.00kt10.00kt20.00kt40.00kt80.00ktd Fplume diameter (m)50040030020010000 2 4 6 8 10 12 14 h 16 18 200plume thickness (m)Figure 4.15: Plume sizes corresponding to different CO 2 volumes. The chosen diameter/thickness pairsare indicated by the black dots. h 0 denotes the tuning thickness for wavelength 60m, d Fdenotes the diameter of the first Fresnel zone at the depth of 2500mWest&offset&(m)&East&depth&(m)&Figure 4.16: The distribution of the CO 2 plumes within the model. The location of the six plumes inMyalup formation is indicated by the red lines. The black vertical lines indicate location ofmodelled monitoring and/or injection wells. The grid size is 0.5km×0.5km.62 |

!me$(ms)$Figure 4.17: Noise-free baseline, monitor, and difference sections with the six CO 2 plumes. All themodelled plumes are clearly detectable on the difference section.synthetic section white noise that is filtered to match the frequency spectrum of the data. The amount ofthe added noise is selected to match the S/N ratio of the real data at an interface close to the modelledplumes. The two left subfigures of Figure 4.18 show the S/N histograms of the real and synthetic data,respectively, at a portion of the data corresponding to the expected injection; the hot colours correspondto higher count of given S/N value at the given time. We tried to match the S/N ratio of approximately 5at the interface between Eneabba Me and Myalup formations.After adding two different realisations of the filtered random noise with realistic levels to the base andmonitor synthetic sections respectively, we see that the detectability of CO 2 plumes in the differencesection is very low (Figure 4.19). In particular, we might be able to detect the two largest plume sizescorresponding to 40k and 80k tonnes of CO 2 , as shown by the zoomed in area in Figure 4.20. This smalldetectability of CO 2 is mostly due to the small effective porosity of 11% in the injection interval.Figure 4.20 also demonstrates the fact that the plumes are more visible on the repeat section comparedto the difference section. This is due to the fact that the noise in the difference section is formed byaddition of two realisations of the modelled noise. However, in realistic scenarios, the plumes wouldbe almost certainly masked by stronger reflections on the monitor section, and would be more likelydetectable on the difference section despite the fact that the difference section contains more noise thanthe monitor section. That the difference section contains noise from both baseline and monitor sectionsopens new research opportunities into data processing workflows that would result in lower noise levelsin difference sections.| 63

Figure 4.18: Histograms of S/N ratio for real (left) and synthetic (middle) sections corresponding to the modelled injection location. Hot colours correspondto high count of the corresponding S/N value at the given time. The S/N ratio for the real section is shown in Figure 4.1. The log 10 scale ofS/N ratio for the synthetic section is shown on the right panel. The amount of noise was added to the synthetic section to approximately matchS/N ratio of 5 at the boundary between the Eneabba Me and Myalup formations.25000 10S/N ratio5 10 15S/N ratio2200 2400 2600 2800 3000 3200 3400CDP200020002000time (ms)10001500time (ms)10001500time(ms)100015005005005000064 |00.20.40.60.811.21.41.61.82

CDP0400 time (ms)800120016002000Figure 4.19: Synthetic baseline, monitor, and difference sections with added noise to match noise levelsin seismic line 11GA_LL2. Only CO 2 plumes of more than approximately 40k tonnes aredetectable. This low detectability is mostly due to the low effective porosity. Figure 4.20: Zoom of Figure 4.19 to the injection interval. Note that the plumes located in the centreof the formation are more detectable on the monitor section than on the difference section.This is due to the fact that the difference section contains noise from both the monitorand baseline surveys. However, plumes located at an interface would not be visible on themonitor section.| 65

4.1.4.3 Marine 2D seismicsWe based our modelling of CO 2 injection on geological model discussed in Section 2.2.3. In-situ formationwater and CO 2 properties are taken from Lemmon et al. (2012). The input parameters for the depth1100 m are based on the log data from well Kyarra 1, located in the area of the interest:• Pore pressure 11.6MPa• Temperature 57°C• Effective porosity 32%The fluid properties at these conditions are:• K CO2 = 0.0248GPa• ρ CO2 = 0.441g/cm 3• K Brine = 2.438GPa• ρ Brine = 0.989g/cm 3We perform the Gassmann fluid substitution, as discussed in Section 3.1.2, using interval P-velocitiesas input. The P-velocities were obtained as an average of velocities from borehole measurements in thearea (Moore and Wong, 2001). Since we do not have measurements of S-velocities, we use Castagna’sequation (mudrock line) for their estimations. For the same modelled injection interval, this gives:Pre-injection: 100% formation water,• V P = 3400m/s,• V S = 1754m/s,• ρ = 2.11g/cm 3 ;Post-injection: 50% formation water, 50% gas mixture, which is the approximate residual CO 2 saturationfor sandstones (Ivanova et al., 2012; Benson et al., 2004) .• V P = 3256m/s,• V S = 1791m/s,• ρ = 2.03g/cm 3This gives the change of acoustic impedance (AI) for P-waves, due to injection, of -564 (m/s)(g/cm 3 ),which is approximately -8% change. The reflection coefficient for normally incident waves to the plumeis approximately 0.044, which means that 0.19% of the seismic energy reflects from the plume.We modelled several CO 2 plume sizes and distributions. These were based on 0.64, 1.59, 3.18, 6.37and 12.73 thousand tonnes of injected CO 2 . At the injection level pore pressure 11.6 MPa, temperature57°C, effective porosity 32%, and saturation of 50%, the respective rock volumes of the CO 2 plumes are9.02 · 10 3 m 3 , 22.56 · 10 3 m 3 , 45.11 · 10 3 m 3 , 90.22 · 10 3 m 3 , and 180.45 · 10 3 m 3 . The dimensions of thefive volumes are shown as black dots on graphs of possible diameter/thickness values for given volumein Figure 4.21, and are summarised in Table 4.3.66 |

5004504000.64kt1.59kt3.18kt6.37kt12.73ktd F350plume diameter (m)3002502001501005000 2 4 6 8 10 12 14 h 16 18 200plume thickness (m)Figure 4.21: Plume diameter vs. thickness graphs for five different CO 2 volumes. h 0 denotes the tuningthickness for wavelength 60m, d F denotes the diameter of the first Fresnel zone at the depthof 1200m. The solid dots indicate the chosen sizes of the plumes.Table 4.3: Dimensions of the modelled CO 2 plumes for Gippsland.Injected CO 2 (’000 tonnes) 0.64 1.59 3.18 6.37 12.73Rock volume with CO 2 (10 3 · m 3 ) 9.02 22.56 45.11 90.22 180.45Plume thickness (m) 2.70 3.66 4.61 5.81 7.32Plume diameter (m) 65.23 88.58 111.62 140.61 177.16| 67

The plumes were distributed along the top of the Latrobe Group in one model for two reasons. First,the simultaneous placement of the different plumes requires computing only one forward model, whichsaves on the time needed to generate the data significantly. Second, the placement of the plumes in onesection allows for easier comparison of detectability of the different CO 2 volumes. The distribution ofthe plumes is shown in Figure 4.22. Figure 4.22: Distribution of the CO 2 plumes in the model. The size of the plumes gradually increasesfrom left to right. The exact sizes are listed in Table 4.3.To model the acquisition using marine streamers, we considered only a subset of the synthetic datacorresponding to shot-receiver offsets of 100-6000m. The noise-free migrated baseline, repeat, anddifference sections are shown in Figure 4.23. The difference section clearly shows the five modelledplumes.To add realistic amounts of noise to the data, we computed S/N ratio using expression (4.1) with 100mswindow on post-stack migrated section of line G92A-3000. To better visualise the S/N values at differentdepths, we plotted histograms of the S/N values for different depths (Figure 4.24).The estimated S/N ratio of approximately 10 in the data at the modelled injection interval was matchedby adding random noise that was filtered to achieve a similar spectrum as the signal. The syntheticmigrated section with added noise is shown in Figure 4.25. The computed S/N ratio for the syntheticsection is shown together with the histogram of the S/N values for different depths in Figure 4.26.After adding different realisations of the same amount of noise that matches the real data to the baselineand monitor sections, we constructed the difference section. The zoom of baseline, monitor, and differencesections are shown in figure Figure 4.27. The difference section indicates that it would be possibleto detect plumes of sizes of approximately 3k tonnes of CO 2 .Another way of achieving realistic noise levels is to consider the change in the signal due to the nonrepeatablenoise. To measure such change, we can use normalised root mean square (NRMS) values.68 |

010020040060080014000.116001800N ratioCDP02200 2300 2400 2500 2600 2700 2800 2900 30000 2200 2300 2400 2500 2600 2700 2800 2900 30000 2200 2300 2400 2500 2600 2700 2800 2900 30000400800time (ms)120016002000 Figure 4.23: Noise-free baseline, repeat, and difference migrated sections. All the modelled plume sizesare clearly detectable on the difference section.00200 100200200180 T (ms) 4006008001000120010 14006008001000120010001200160101401201001801400160014001600600.14018001800200 10 20 30S/N ratio0.0112002200 13002300 14002400 15002500 16002600 17002700 18002800 19002900 20003000 20 30 1200 1300 1400 1500 1600 1700 1800 1900 20000.01Figure 4.24: On the right is S/N ratio computed with 100ms window on real data. On the left is thehistogram of S/N ratio for each time slice, where the hot colours correspond to high countof S/N values.| 69

0 20040060080010001200140010.50-0.516001800-1 2200 2300 2400 2500 2600 2700 2800 2900 3000 Figure 4.25: Synthetic section with added noise to match S/N levels of the real data.0010020020040060040060010T (ms) 80010001200 80010001200114001600140016000.1180018000 10 20 30S/N ratio2200 2300 2400 2500 2600 2700 2800 2900 3000 0.01Figure 4.26: On the right is the S/N ratio computed with 100ms window on synthetic section with addednoise. On the left is the histogram of S/N ratio for each time slice. Hot colours correspondto a high count of S/N values.70 |

Figure 4.27: Zoomed in synthetic baseline, repeat, and difference migrated sections with added noise tomatch S/N ratio at the injection interval of the real data. Only CO 2 plumes of more thanapproximately 3k tonnes are detectable.NRMS values for two traces are computed using the following equation (Kragh and Christie, 2002):NRMS =RMS(a − b) · 200%, (4.2)RMS(a)+RMS(b)where a and b are time windows for the two traces and RMS denotes root mean square (i.e. L 2 -norm)of the windowed trace. Typical NRMS values for marine time-lapse surveys range from 20% to 50%(Calvert, 2005). To consider worst-case scenario, we try to achieve NRMS value of approximately 50%.To this end, we add one realisation of frequency-matched noise to each baseline and monitor sections andcompute NRMS values. We change the strength of the added noise till we achieve the desired NRMSvalues. The histogram of NRMS values for different times and the NRMS values for each trace areshown in Figure 4.28. The amount of noise that approximately matches NRMS of 50% is very similarto the noise level with S/N ratio of 10. The baseline, monitor, and difference sections with the matchedamount of noise are shown in Figure 4.29. Due to the similar amounts of noise, the difference sectionsin Figure 4.27 and Figure 4.30 indicate the same level of CO 2 detectability, namely possibility to detectplume sizes corresponding to approximately 3k tonnes of CO 2 .| 71

00200200200180400400160600600140 T (ms) 80010001200 80010001200120100801400140060160016004018001800200 100 200NRMS (%)2200 2300 2400 2500 2600 2700 2800 2900 3000 0Figure 4.28: On the right is the NRMS attribute for the synthetic section with added noise. On the left ishistogram of NRMS for each time slice. Hot colours correspond to a high count of NRMSvalues. The level of noise was chosen to produce 50% NRMS in the injection interval.CDP2200 2300 2400 2500 2600 2700 2800 2900 30000 2200 2300 2400 2500 2600 2700 2800 2900 30000 2200 2300 2400 2500 2600 2700 2800 2900 3000000400800time (ms)120016002000Figure 4.29: Synthetic baseline, monitor, and difference sections with added noise to approximatelymatch NRMS of 50%.72 |

Figure 4.30: Zoomed view of the injection interval of Figure 4.29. Only CO 2 plumes of more thanapproximately 3k tonnes are detectable.| 73

4.1.5 3D Reflection seismicsSo far in this report, all seismic modelling has assumed a reduced dimensionality in source, receivers andmodels. Such modelling is useful for ’back of envelope’ calculations where it is important to see grosseffects. Because of the subtleties of modelling CO 2 plume propagation, any realistic M&V project willneed to use models that are as close an approximation to the real world as possible. Such models canreally only be developed using 3D seismic surveys.3D Seismic surveys utilize multiple points of observation, viz. geophones and sources in a grid, tosample the spherical wavefronts that are characteristic of seismics. Since the entire wave field is sampled,diffractions from fault boundaries and other discontinuities can be traced to their source, resulting inimages which typically have better structural definition and better S/N ratios than 2D seismic surveys.This means that models built from 3D seismic surveys will, in general, be more accurate than 2D models.More well logging data will be required in order to test variation of physical properties over the reservoir.The benefit to such data is that realistic reservoir models can be constructed, and these models can beused with geological and flow modelling packages in order to evaluate injection scenarios. Such modelscan be updated over the life of the project. An example of the utility of 3D seismics over the Ketzinproject was given by Yordkayhun et al. (2009).3D seismic data were not available to construct either the SW Hub (Section 2.1.3) or CarbonNet (Section2.2.3) conceptual models. Devising realistic 3D models for either project is a requisite, significanttask as these projects mature. However, given that both projects are very much in their infancy, and muchmore data are to be collected, developing such models was deemed beyond the scope of this study.74 |

4.1.6 Permanent receiver installationsAll seismic surveys discussed so far have assumed that sources and receivers can be repositioned in orderto properly sample the volume of interest. They are not the only form of seismic surveying. Permanentinstallations have many advantages over their moving counterparts, but clearly, they can only be effectivelypositioned after a considerable amount of knowledge of the reservoir and likely plume migrationpathways has been gained. This section discusses the application of permanent receiver installations toCO 2 M&V at SW Hub and CarbonNet CCS flagship projects.4.1.6.1 Vertical seismic profilingTo investigate alternative approaches to surface seismic monitoring for SW Hub, we modelled a permanentlyinstalled receiver geometry in several vertical wells of varying depth. This vertical seismicprofiling (VSP) receiver geometry consisting of several wells has several potential advantages over thestandard surface receiver spreads:• Permanently installed receivers can take advantage of potentially weaker but repeated sources• Permanent installations have fewer issues related to time-lapse changes in acquisition parameters• Underground placement of receivers aids in increasing of S/N ratio by eliminating surface relatednoises.• Permanent installations help reducing the impact of repeated surveys on community.The modelled well locations are shown in Figure 4.16, with the spacing between the wells of 1,000 m.The spacing of the receivers in the wells is 10m and the spacing of surface sources is 50m. The use ofthe relatively deep (2-3 km) wells in the model allows us to decimate the data to simulate acquisitiongeometries with varying well depths. We number the wells from 1 to 6 from left (West) to right (East).The plumes are clearly visible on noise-free data on zero-offset (100m offset) sections for different wells,as shown in Figure 4.31 for well 6 and Figure 4.32 for well 5.The migrated sections were constructed by Kirchhoff migration with aperture of 10 degrees assuming thereflector dip of 10 degrees. Figure 4.33 shows the migrated images of the baseline and monitor datasetsfor well 5, using the entire well depth (receiver range of 50-2950m). The amplitude of the signal on thedifference section is approximately 10% of the amplitude of the main reflector on the baseline section; itis clearly visible only on the difference section. Again, if we added different realisations of noise to thebaseline and monitor sections, the noise on the difference section would be consisting of the two noisesand thus would be stronger than the noise on the repeat section. However, due to the presence of muchstronger reflectors on the monitor section, the plumes would be possibly visible only on the differencesection.It is important to note that very large offset walkaway VSPs are not practical for standard processing.However, for difference sections, the use of large offsets might be beneficial, as the issues associatedwith the long offsets are repeatable and will subtract out.To study different well depths for monitoring was done by limiting the range of receivers in the modelleddata. Figure 4.34 shows the effect of using wells of 3000m, 1000m, and 500m depth for monitoring; the| 75

Figure 4.31: Well 6, Zero-offset (100m offset) noise-free VSP baseline, monitor and difference sections.The reflected wave is visible on the monitor section only because the modelled plume isnot located at an interface. Figure 4.32: Well 5 Zero-offset (100 m offset) noise-free VSP baseline, monitor, and difference sections.The arrows indicate the locations of the plume on the difference section.76 |

Figure 4.33: Well 6, Zero-offset (100m offset) noise-free VSP baseline, monitor and difference sections.The amplitude of the difference signal is only approximately 10% of the amplitude of thereflector on the baseline section.degradation of the quality of image is small compared to the benefits of using shallower wells. The mainbenefit of shallower wells is considerably smaller costs associated with drilling. The main conclusion tobe drawn from this modelling is the potential of using a relatively-dense (say 500 to 1000 m) array ofshallow (between say 500 and 1000 m deep) wells for permanent installation of receivers.The noise level related to repeatability depends on number of shots for a given VSP. Campbell et al.(2011); Pevzner et al. (2010) and Pevzner et al. (2011) conclude that repeatability (in terms of NRMS)of a single offset VSP is similar to 3D surface seismic survey. Using multiple sources in form of sparsewalkaway/3D along accessible roads should only improve this repeatability. However, exact estimationof noise in time-lapse sense together with a design of the survey is going to be site dependent and requiresa separate study.| 77

Figure 4.34: Walkaway VSP difference sections for receiver depth ranges 50-2950m (left), 50-1000m(middle), and 50-500m (right). The source-offset range is ±1500m. The degradation ofdetectability is small compared to savings associated with using shallower wells.78 |

4.1.6.2 Ocean bottom cablesFor marine seismic, we can consider using ocean bottom cables (OBC) instead of using towed streamersfor repeated surveys. We model OBC for the CarbonNet setting. Since we do not have real data fromOBC in the Gippsland Basin, we cannot estimate the amount of noise in the data. However we know fromthe literature that for OBC data, the 4D NRMS (as defined by expression (4.2)) values are commonly10-20%, with 7% achieved in Shell’s Shearwater project in 2002/2004 (Staples et al., 2006). To model such repeatability, we added two noise representations with matching frequency content to the baselineand monitoring surveys that match NRMS of 17%. Figure 4.35 shows the synthetic baseline section withadded noise to approximately match NRMS of 17% at the injection interval.0200400200180180400400 8001601601200 600140 140time (ms)8001200time (ms)1600 8001000 20001200 400120100801400 8006016001600 120040 401800 16002020200050 100 150 200NRMS (%)200050 2200 2300 200 2400400 2500 2600 600 2700 2800 290010003000CDP0Figure 4.35: On the right is the NRMS attribute for the synthetic section with added noise. On the left ishistogram of NRMS for each time slice. Hot colours correspond to a high count of NRMSvalues. The level of noise was chose to produce 17% NRMS in the injection interval.We migrated the noisy sections using the model velocities. The results for the baseline, monitor, and differencesections are shown in Figure 4.36. The figure demonstrates that using OBC increases detectabilitywhen compared to the marine streamers; of all the modelled plume sizes are detectable, including0.64 kt of CO 2 . The effect of the true velocities on multiples is demonstrated by the wide spread of themultiples in the migrated sections. These, however, correspond to repeatable multiples, and as such willsubtract out from the difference section. The potential issue of non-repeatable multiples due to changesin temperature and/or tide could be addressed by using the OBC receivers as virtual sources (Bakulinand Calvert, 2006).| 79

CDP CDP2200 22002300 23002400 24002500 25002600 26002700 27002800 28002900 290030000 300002200 22002300 23002400 24002500 25002600 26002700 27002800 28002900 290030000 300002200 22002300 23002400 24002500 25002600 26002700 27002800 28002900 2900300000 0400 400800 800timetime(ms)(ms)1200 12001600 16002000 2000Figure 4.36: Synthetic baseline, monitor, and difference sections with added noise to approximatelymatch an NRMS of 17%.80 |

4.2 Electromagnetic surveying & interpretation4.2.1 Introductory remarksAlthough electrical and electromagnetic (EM) methods have a long history of use in hydrocarbon exploration,in fact, predating seismics, their use in CCS is perhaps unfamiliar. Because of this, someexplanation of the underlying theory is warranted.All EM phenomena are described by Maxwell’s equations. In the frequency domain, with bold caseindicating vector fields, these can be written (e.g. Ward and Hohmann, 1991)∇ × E + iωµH = 0∇ × H − (σ + iωε)E = 0∇ · B = 0∇ · D = ρ(4.3)where E is electric field (in V/m), H is magnetic flux (in A/m), B is magnetic field (in T), D is theelectric displacement field (in N/Vm), ω is angular frequency (2 πf), ε is electrical permittivity, µ ismagnetic permeability, σ is electrical conductivity, ρ is electric charge density and i is √ −1. At typicaloperating frequencies, the key quantity in Equation 4.3 is conductivity. EM Prospecting essentially mapscontrasts in conductivity (or its inverse, resistivity). For the majority of CO 2 applications, these contrastswill be caused by resistive CO 2 displacing more conductive groundwater. Different models of electricalconductivity were discussed in Section 3.3.Electromagnetic data may be analysed in either of two domains viz. the time domain, in which fieldsare analysed as a function of time or the frequency domain, in which fields are analysed as a function offrequency. The two domains are linked through the Fourier transform (e.g. Lighthill, 1980; Bracewell,1986). Advantages and disadvantages of the two domains are summarised in Table 4.4. Unless otherwisestated, in this report, we consider frequency domain results.Table 4.4: Advantages and disadvantages of frequency and domains in electromagnetic prospecting.Domain Advantage DisadvantageFrequency Maximum S/N ratio Large primary fieldSimple interpretationSelect depth with frequencyTime Range of depths with one signal Lower S/N ratioMeasure without primary fieldElectromagnetic data may be acquired using many different survey configurations (Spies and Frischknecht,1991). Because only a few configurations are appropriate for optimal detection, examination of the electromagneticresponse of a CO 2 plume is examined by survey configuration. Sources and measured fieldsare three-dimensional, so that model responses are correctly approximate.| 81

4.2.2 General comments on induction loggingInduction logging is a controlled source electromagnetic technology that is routinely used by the hydrocarbon,groundwater and geothermal industries. Time lapse induction logging is also rapidly becominga proven technology for time lapse monitoring of the movement of an injectant (e.g. water or CO 2 ) pastmonitoring wells proximal to an injection wells. A well developed case study for application of timelapse induction logging at an aquifer storage and recovery site, including forward modelling and fieldexamples, can be found in Malajczuk (2010). The value of time lapse temperature and induction loggingas a constraint on numerical modelling for flow, solute transport and heat transfer is expressed inLeong et al. (2012). An example of time lapse logging applied to CO 2 injection can be found in Xueet al. (2006). The above examples use a close spaced vertical magnetic dipole source and receiver tocomplete induction logging, however the latest tools are capable of recovering tensor conductivity withtransmitting and receiving antenna oriented in three directions. A review of modern induction and resistivitylogging equipment can be found in Davydycheva (2010). Harris and Pethick (2011) compare avertical electrical dipole antenna with a vertical magnetic dipole antenna for time lapse in-hole CSEMmonitoring and Xue et al. (2006), considers energising steel casing to create an electrical bipole source.It follows that some key subjects that may require attention prior to implementation of a time lapseinduction logging program are:• In general acquisition should be from within an open-hole or a non-conducting (e.g. non-metallic)monitoring well. Direct communication with the formation (i.e. slots) is not required. However,any metallic or conducting materials within the monitoring installation may make quantitativeanalysis of results problematic.• Standard induction logging tools can be used. However strict calibration procedures must be inplace.• The transmitter frequency should be chosen to suit the geoelectrical setting. In general, frequenciesbetween 10 and 200 kHz should be suitable.• Time lapse temperature logging must be completed in parallel with the induction logging.• A plan for processing and inversion of field data to recover true formation resistivity should be inplace since the induction logging instrument can only provide magnetic fields or apparent resistivity.These need to be converted to true formation resistivity.• A data calibration plan should be in place. Calibration points should be set up so that the relationshipbetween formation resistivity and CO 2 saturation is established. These would be at selecteddepths in selected wells. The upper intervals of the monitoring well (i.e. no CO 2 ) are importantfor calibrations (i.e. locations where resistivities will remain the same).• Calibration wells should be identified.• A plan for integrating recovered resistivity with reactive transport numerical modelling of the CO 2injection should be considered.82 |

• Clearly, the induction monitoring wells should be in the path of the injected CO 2 . In this case thecomposition of the well and its ability to resist to chemical attacks must be carefully considered.The concept behind time lapse induction logging is relatively simple. A monitoring well is placed in alocation sufficiently close the injector well for the injected CO 2 to pass the well. The well should be nonmetallicand could potentially be fully cemented from top to bottom to ensure the risk of seepage aroundthe monitoring well is negligible. Time lapse induction and temperature logging should be completedat sufficient frequency for any change in formation resistivity and temperature associated with CO 2injection to be detected. Figure 4.37 provides a schematic illustration indicating how induction loggingis able to scan the entire injection interval and capture the passage of injectant at levels that may not beexplicitly captured by pumping from small screened interval of a monitoring well. That is time lapselogging can capture the passage of injectant at all vertical depths along the well.Figure 4.37: Schematic of CO 2 injection.Figure 4.37 illustrates a possible scenario where higher resistivityCO 2 injectant moves along fast-flowpathways (i.e. high permeability sand layers) and is detected by a multi-frequency, multi-offset verticalmagnetic dipole induction logging tool.Multi-offset, multi-frequency, full tensor induction logging would be ideal for time lapse logging. Whilesuch systems do exist (i.e. X, Y, Z coil transmitters and receivers), the arrangement in Figure 4.38(vertical magnetic dipole transmitter and receivers) would be sufficient to detect injectant passing thewell.| 83

Figure 4.38: Schematic of time lapse injection logging detecting CO 2 injected. Red lines indicate CO 2distribution which will be complex and highly dependent upon permeability distribution.84 |

Key points to consider for application of induction logging technologies to monitoring and verificationof CO 2 injected during geo-sequestration are:• Time lapse induction logging is a proven technology for monitoring the fate of an injectant. Theinduction logging technology is a high frequency CSEM technology that is used for monitoringthe distribution of many types of injectant. It is capable of recovering the vertical distribution ofinjectant as it passes a suitably designed monitoring well.• The time lapse monitoring well locations must be located within the path of the injectant. Theinduction logging technique is a high frequency CSEM technology and in most circumstanceshas limited penetration into the formation. For time lapse induction logging the monitoring wellclearly needs to be placed within the path of the injected CO 2 .• The monitoring well must be designed to accommodate application of induction logging methods;i.e. , the monitoring installation must be non-conductive. Fiberglass reinforced polymer (FRP)wells are commonly used as monitoring wells for monitoring of sea water intrusion.• For induction logging, direct communication (e.g. slotted casing) with the formation is not required.However, for general purpose CSEM logging, open area to the formation is preferred(e.g. slotted FRP casing over the injection interval).• The ideal time lapse induction logging tool would accommodate multi-frequency, multi-separation,multi-orientation measurements: The high end Schlumberger induction logging tool does have thepotential to achieve this. Such tools provide the possibility of recovering full tensor formation conductivity.However, in many cases a vertical magnetic dipole source and receiver system wouldsuffice.• The transmitter frequency should be chosen based on the resistivity of the injection interval. Typicaltransmitter frequencies for induction logging are between 10 and 200 kHz. High frequencytools should in general be used for resistivity formations (e.g. greater than 100 Ωm). Lower frequencytools should be used for conductive formations (e.g. less than 5 Ωm).• Temperature should be measured simultaneously: Changes in temperature will impact on electricalconductivity. Injection of CO 2 will almost certainly change formation temperature. Time lapsetemperature logging will therefore provide an important constraint on numerical modelling. Forexample, temperature should be included in any multiphase reactive transport modelling as it mayimpact on the rate at which chemical reactions occur.• The complete magnetic field should be recovered from the logging. Many logging tools recovera calibrated value of electrical conductivity. This calibrated value of electrical conductivity is anapparent resistivity. It is not the formation resistivity. To recover formation resistivity, the magneticfields recovered from the induction logging must be inverted to obtain formation resistivity. Theinversion algorithm may need to incorporate a 3D forward modelling engine that is capable ofincluding full well and formation geometry.| 85

• Final conversion of induction logging measurements to formation resistivity will require sufficientinformation concerning formation petrophysics. These are recovered from core measurements andfrom wire line logging (e.g. NMR, Neutron, etc).• An alternative to time lapse induction logging is permanent or semi-permanent in situ deviceswhich measure electrical properties, temperature and/or water chemistry directly. These types ofmulti-node devices either exist or can be developed. These could potentially be installed behindcasing leaving the possibility of time lapse logging from within casing. In situ devices have severaldisadvantages. Instrument failure could risk the monitoring program. Also, they do not accommodatenew technology.86 |

4.2.2.1 Single-borehole electromagneticsThe combination of deep targets and electromagnetic field falloff – O(r –2 to r –3 ) almost requires downholeelectromagnetic surveys. The simplest such survey consists of a magnetic dipole transmitter leadinga magnetic dipole receiver by a fixed distance as in Figure 4.39. Such a configuration is useful for obtaininginformation about changes in medium. It is possible to obtain some information about the distanceto target, though clearly, it is not possible to ascertain that target’s direction.Figure 4.39: Single borehole EM survey configuration. More than one receiver can be used at differentspacing and/or orientation.The initial model used for EM studies is 1D and derived from seismic studies in section 4.1.3. Assuch, modelling in this section represents a ’best-case’ scenario. Equation 3.1 was used to derive layerresistivity using Archie’s law (Equation 3.8). Brine salinity was assumed to be 30 000 ppm which wasconverted to conductivity using the chart in Figure A.1. Structural and lithological uncertainty is implicitin these EM results from the use of layer boundaries derived from seismic and well-log data. Not shown,are raw data used to construct Figure 4.40. These suggest the need to acquire data with a noise floor below1 ppm, and this will be difficult, especially at lower frequencies, even using SQUIDs (super conductingquantum interference devices) as receivers instead of induction coils (Du et al., 2004).We investigated CO 2 changes of 2, 5, 10 and 20% in the Wonnerup. Results are presented in Figure 4.40and show that the change in EM response increases with increasing CO 2 saturation. That the EM signalchanges with changes in CO 2 indicates that such an EM system might be suitable as a rapid method ofmonitoring the upper bound of a CO 2 injection plume. It is coincidental that percentage changes for the10 kHz signal almost mirror percentage change in EM response.| 87

(a) Single borehole EM Sensitivity to CO 2 saturation05001000Depth m15002000250030000.20 0.25 0.30 0.35Resistivity m(b) Underlying modelFigure 4.40: Sensitivity of EM Modelling to changes in CO 2 saturation. Graphs plot the effect of increasingCO 2 saturation to 20% in the Wonnerup as percentage change in EM response.These graphs show that a single drillhole EM system may be able to monitor changes inCO 2 saturation. Grey lines on each graph represent layer boundaries in the original seismicmodel.88 |

4.2.2.2 Separated-borehole electromagneticsSection 4.2.2.1 showed that EM methods could be used to monitor changes in resistivity caused byan increase in CO 2 . Because a single borehole was used, determination of the direction in which thosechanges are occurring is difficult. In order to ascertain the spatial location of resistivity changes, multiplesurveys are required in multiple boreholes. Such surveys are also called crosswell EM surveys. Becauseof the O(r –2 to r –3 ) falloff in field-strength magnitude, these boreholes must be located reasonably closetogether. Al-Ali et al. (2009) suggest that maximum intrahole distances of 300 m when both holes arecased, and 1 km when both holes are case using non-conductive material such as FRP, carbon-fibre orchrome steel. Wilt (2003) suggests that the effects of steel casing can be compensated for by a combinationof modelling and measurement. The effect of steel-cased boreholes is illustrated in Figure 4.41where peak moment attenuation is shown to be frequency dependent over a typical measurement range.Figure 4.41: Effects of metallic casing in crosshole EM surveys (after Wilt, 2003). The casing is a5 " diameter, 3/8 " thick steel casing. The effect of the casing (seen in the red curve) isa frequency-dependant attenuation of peak moment. A combination of measurement andmodelling is required to ameliorate casing effects.Separated-borehole (or cross-hole) surveys are designed to map distributions between two (or more)boreholes and have successfully been used for reservoir characterisation and monitoring steam floodingevents (Wilt, 2003; Patzek et al., 2000; Wilt et al., 1995). Typical systems are based on a design proposedby Wilt et al. (1995) which was developed in the 1980’s and uses inductive sensors to measure magneticfields. Though typical surveys are along sub-vertical boreholes, the method is not restricted to these.Horizontal boreholes may be used; indeed, it is arguable that such a configuration above and below anappropriate aquifer would give better maps of a CO 2 plume’s lateral extent than many vertical boreholes.A typical separated borehole prospecting system is illustrated in Figure 4.42 where transmitters andreceivers are pulled up each borehole so that the target zone can be fully explored.| 89

Induced currentsTransmitter wellPrimary fieldTargetSecondary fieldReceiver well> Primary and secondary fields. An alternating current excites the magnetic dipole transmitter coil tosend an electromagnetic field into the formation. This primary field induces eddy currents that, in turn,generate a secondary alternating electromagnetic field whose strength is inversely proportional toformation resistivity. The secondary electromagnetic field is detected at the receiver array along withthe primary field.Figure 4.42: Typical crosshole EM survey configuration (after Al-Ali et al., 2009). The combination ofvertical dipole transmitter and receiver optimises the survey to map horizontal structures.Multiple frequencies are used ranging from 5 Hz to 1 kHz.The applicability of a separated borehole EM survey to CO 2 hinges on its ability to detect changes inresistivity and volumetric extent within a conductive aquifer. To simplify comparison between singleborehole results from Section 4.2.2.1, we modelled the same set of transmitter frequencies and the samelayered earth. A single transmitter borehole with nine separate transmitter locations covering the Myallupand Wonnerup injection zones was used. The response was calculated in boreholes spaced 100, 300, 500and 1 km away from the transmitter borehole. Target variation for this study is listed in Table 4.5.ParameterThe DeepLook-EM transmitter generates a A dataset may span 30 to 60 receiver stationsmagnetic field that is sent into the formation at and frequently constitutes several thousand measurements.The strength of the secondary signal isuser-defined frequencies between 5 Hz and 1 kHz.This magnetic field, known as the primary field, quite small, so to reduce the signal/noise ratio, theattenuates with increasing distance from the incoming signals are stacked and averaged overtransmitter. The primary field induces a current in several hundred cycles per station. Depending onconductive formations (above). This current generatesan opposing secondary electromagnetic frequency, transmitter logging speeds may rangethe amount of averaging and the transmissionfield, such that the total field decreases in amplitudeTable with 4.5: decreasing Parameter formation variation resistivity. for 7 An separated typical deployment borehole requires modelling roughly 12 study to 30 hoursfrom 600 to 1,520 m/h [2,000 to 5,000 ft/h], and aarray of four sensitive induction-coil receivers is of field recording for a vertical section of 300 mstationed in the receiver borehole to detect the [1,000 ft]. The distance covered by the four evenlyVariationprimary electromagnetic field generated by the spaced coils in the receiver array helps reducetransmitter and the 750, secondary 1000, electromagnetic 1100, 1200, logging 1250, times 1300, by 1400, spanning 1500, a wide 2000 interval m forfield generated by the100,induced300,currents.500 8& 1000 meach station.Figure—05Transmitter depthReceiver borehole offsetTarget dimensionsTarget resistivity40 × 40, 100 × 100, 200 × 200, 500 × 500 & 1000 × 1000 m101, 105, 110 and 120% more resistive than background horizonTransmitter Well Receiver Well Maximum Well SpacingOpen holeFiberglass casingOpen holeOpen holeChromium steel casingOpen holeFiberglass casingChromium steel casingCarbon steel casingChromium steel casing1,000 m [3,280 ft]1,000 m [3,280 ft]500 m [1,640 ft]450 m [1,476 ft]350 m [1,148 ft]Three examples of crosshole EM response are presented. The first (Figure 4.44) illustrates falloff fromsource with intrahole distance. Response amplitudes are seen to falloff rapidly as intrahole distanceincreases. This suggests that multiple vertical holes might be required for a crosshole EM M&V systemto be effective. A compromise might be to use a horizontal hole, perhaps 100 m above the target horizonor seal. Such a geometry > Distance wouldversus have casing. the DeepLook-EM advantage surveys of easier are constrained delineation by casing of areal extents. However,type and formation conditions.unless elevations of individual aquifers can be constrained, a horizontal drillhole system would find itdifficult to resolve vertical CO 2 movement. Reduction in amplitude with increasing intrahole distances isnot so great an issue when signal amplitudes are large. However, for small amplitudes, very high qualitydata are required to distinguish time-lapse signal variations from noise.Assessing Critical Survey ParamNo two crosswell EM surveys are tis influenced by local conditionscharacteristics, distance betweand receiver wells, formation cowellbore deviation—all of whichquality of signals detected at thinterval to be logged, logging speeter frequency can also affect theresponse to various formation pthis reason, every survey is desigfor the unique combinationimposed by any given well pair.In all but the most competenone very common survey constraisteel casing. Electromagnetic signsevere attenuation as they diffucasing. 9 Attenuation increasesconductivity, casing magnetic petransmitter frequency. Steel calimits the range of frequenciescrosswell EM survey. Such casingeight orders of magnitude more cthe surrounding formation, hindesion and reception of high-frequenconstrains the ability to resolve tdistribution in the interwell regioOther components of the caalso degrade crosswell EM data. Apresent in collars and centrincreases the casing effect and cmeasurement. When the positionponents can be determined priorsurvey, their effects can be nearlcareful sensor positioning anFortunately, DeepLook-EM loggdetect the locations of collars andhelp logging crews recognize anareas where the casing effects areBecause casing effects also limdistance across which meaningfulbe obtained, these effects mustadvance of the survey. To this end,materials have been tested (left).and nonmagnetic fiberglass casintransparent to the transmitterAcquiring data through this casinglogging in open hole. On the othesteel casing is both highly conducmagnetic, so it poses the worst-casfiberglass casing is not feasible,casing may be recommended ovinterval because it is nonmagnetiductive than carbon steel.90 |4225612schD6R1.indd 5

The second and third examples of crosshole EM response are coupled. Figures 4.45 and 4.46 compareanomalous responses in different drillholes as the resistivity in a target volume increases, representingthe filling of this volume by CO 2 over time. As CO 2 saturation increases, more subtleties are evident inthe EM response, particularly at higher frequencies.| 91

2500 2500(a) 40 × 40 m CO 2 plume in Myallup500Easting m(b) 500 × 500 m CO 2 plume in WonnerupFigure 4.43: Two representative cross-hole EM models. Figure 4.43(a) shows a 40 × 40 m plume whileFigure 4.43(b) shows a 500 × 500 m plume filling the Wonnerup then extending laterallyeast and west. Dark grey lines at constant depth indicate layer horizons from Figure 4.40.The red vertical line indicates the drill hole down which transmitters are put while the fourblack vertical lines (at 100, 300, 500 m and at 1 km) are receiver drill holes. Transmitterlocations are indicated by red dots and the receiver locations are indicated by grey dots(down the 1 km drill hole only). All drill holes were modelled in-plane. The injection zoneis in the centre of the appropriate layer along 50 East, midway between the transmitter andthe drillhole at 100 m.92 |

0 A 100 m 50010001500 2000 250010 9 8 7Log 10 AResponse falloff with borehole distanceRL m0.10 Hz0.32 Hz1.00 Hz3.16 Hz10.00 Hz31.62 Hz100.00 Hz316.23 Hz1000.00 Hz3162.28 Hz10000.00 Hz B 300 m C 500 m10 9 8 7Log 10 A10 9 8 7Log 10 A D 1000 m10 9 8 7Log 10 AFigure 4.44: Response falloff of crosshole EM system with distance from source. Distances between thetransmitter and receiver drill holes is indicated in the captions to Figures a, b, c, and d. Darkgrey lines at constant depth indicate layer horizons from Figure 4.40. This figure shows thatlower frequencies are required as intrahole distances increase and that as lower frequenciesare used, anomaly features become broader making it diffcult to resolve fine layers. Thisfigure also shows that, for the modelled earth, there is little point using frequencies higherthan 1 kHz, even for close drillholes.| 93

0Wonnerup, 1000 m target; Tx 1200m ; DDH 100m ; Vertical component A 101 B 105 C 110 D 120 5001000RL m1500 20000.10 Hz0.32 Hz1.00 Hz3.16 Hz10.00 Hz31.62 Hz100.00 Hz316.23 Hz1000.00 Hz3162.28 Hz10000.00 Hz 250010 9 8 7Log 10 A10 9 8 7Log 10 A10 9 8 7Log 10 A10 9 8 7Log 10 AFigure 4.45: Crosshole EM response dependence on target resistivity for a 500×500 m target filling theWonnerup, representing a plume in which CO 2 increases in saturation over the target volume.Figures a, b, c and d represent increasingly later vintages. In contrast to Figure 4.46,this drillhole is 100 m away from the transmitter drillhole, and anomalies are larger as aresult. The transmitter elevation is indicated by the red dashed lines. Ordinate and abscissaaxes are in units of log 10 ratio (to baseline) and m respectively. This figure shows that largervariations in target resistivity are easier to detect than smaller ones.94 |

0Wonnerup, 1000 m target; Tx 1200m ; DDH 300m ; Vertical component A 101 B 105 C 110 D 120 5001000RL m1500 20000.10 Hz0.32 Hz1.00 Hz3.16 Hz10.00 Hz31.62 Hz100.00 Hz316.23 Hz1000.00 Hz3162.28 Hz10000.00 Hz 250010 9 8 7Log 10 A10 9 8 7Log 10 A10 9 8 7Log 10 A10 9 8 7Log 10 AFigure 4.46: Crosshole EM response dependence on target resistivity for a 500×500 m target filling theWonnerup, representing a plume in which CO 2 increases in saturation over the target volume.Figures a, b, c and d represent increasingly later vintages. In contrast to Figure 4.45this drillhole is 300 m away from the transmitter drillhole, and anomalies are much smalleras a result. The transmitter elevation is indicated by the red dashed lines. Ordinate andabscissa axes are in units of log 10 ratio (to baseline) and m respectively. This images showsthat larger variations in target resistivity are easier to detect than smaller ones.| 95

4.2.2.3 Vertical electric bipole systemsTime lapse monitoring with a high powered vertical electric bipole will successfully detect the large scalemovement of CO 2 injected into most formation water filled reservoirs, provided a suitable monitoringwell network is installed. The below are considerations for developing a CSEM monitoring network.• The key consideration is the proximity type and orientation of CSEM transmitters and receiversrelative to the target injected CO 2 .• A vertical electrical bipole (VED) requires direct communication with the formation.• The ideal vertical electrical bipole monitoring network would include deep specifically designedCSEM monitoring wells that span the injection interval spaced at increasing separation from theinjection wells.• A cost effective alternative to deep CSEM monitoring wells is to set up a monitoring network ofshallow wells above the injection zone. That is less expensive shallow CSEM monitoring wellsmay be a reasonable compromise. These could be used in combination with time lapse surfacemeasurements• The ideal VED monitoring well would allow electrical current to flow from the vertical electricbipole directly into the formation. The can be achieved with shallow FRP wells with slotted intervals.Such wells could be used for many different time lapse CSEM monitoring configurations.Installation of wells can be staged according to cumulative injection i.e. wells should be located atprogressively greater distances over the life of the field.• EM monitoring installations should be non-metallic and be open to the formation over key intervals.A combination of slotted and plain high-strength FRP or some equivalent, would be ideal.• Magnetic dipole sources and receivers will be effective over the full FRP cased interval (the entireinstallation). Slotting is not required for monitoring with a magnetic dipoles transmitter.• Using combinations of magnetic dipole and electrical bipole transmitters provides the possibility ofrecovering time-lapse tensor conductivity changes related to injections of CO 2 . By driving currentin different directions through the formations, an indication of tensor conductivity can potentiallybe obtained.• Signal digitization at the receiver can provide extremely low-noise continuous recording of EMfields within the well. That is the a suitably designed well environment may represent a very lowelectrical noise setting where signals in the Nanovolt range may become possible.In summary, time-lapse in-hole and cross-well controlled source electromagnetic methods have the potentialto be highly effective methods for recovering CO 2 distribution during sequestration providedinstallations (i.e. monitoring wells) and instrumentations (receivers and transmitters) are correctly designedfor this purpose. Surface methods may be effective although they can only provide a very lowresolution outcome and, if used in isolation, become increasingly problematic at depths greater than2000 m.96 |

Downloaded 23 Sep 2010 to 130.116.147.18. Redistribution subject to SEG license or copyright; see Termdistance z, related to the well-known skin depth z s :simple model studies invariaty and anisotropy of the hoz s 1/ 1/ 2/o . 4 sumptions or additional infomight be used to generate theThe skin depth is the distance over which fiel amplitudes are reducedMarineto 1/e in electromagneticsa uniform conductor, or about 37% given by , andrestricted to layered models4.2.3good results. The corollary ithe phase progresses one radian, or about 57° given by . SkinMarine electromagnetic (mCSEM) surveys were first proposed by Bannister (1968) asformation seafloor resistivitysurveys. For the next 30 years, such surveys were largely academic exercises until olution 2000 when significantl Ei-.into the interpretatdepth is fairly well approximated bydesmo et al. (2002) described a marine 500 meters electromagnetic survey conducted explicitly for Although hydrocarbon going from the wzexploration. Constable (2010) s , 5 represents a substantial losgives a comprehensive freview of the method. Because of the success ofWhen the frequency goes tomCSEM surveys in hydrocarbon exploration, it is appropriate that they be investigated for CO 2 M&V.Løseth where (2010) ordinary presents frequency a readable f exposition /2.equationof the method’s theoretical basis. Because EM methods,otherUnlikethan magnetotellurics,heat flo , foruseEMcontrolledinductionsources,the frequencythe term ’CSEM’of the forcingwhich has been used in literaturefunction is under the control of the geophysicist and provides an intrinsicsensitivity to depth. However, when one progresses from the which describes potential-fieis a misnomer; mCSEM is used in this report.Marine waveCSEM equation surveys to the occupy diffusion a nicheequation, in modern the exploration concept programs of resolution where their primary comesapplicationalmost nonexistent.ischanges as a risk mitigation drastically. tool, Forcomplimenting a harmonic excitation, seismics. Figure the entire 4.47 presents earth/sea/ a schematic plained of an mCSEM by an arbitrarily thinsurvey. air system Typically, is excited seafloor byreceivers EM energy, are used and and what theistransmitter measuredisat towed the receiveras is aseawater kind ofisaverage conductive, of the low whole (typically system

seasurface (at the speed of light) before diffusing into the water column. The airwave dominates themCSEM response at large distances, but the term ‘large’ depends on the height of the water column andthe method is less effective in shallow (

ceiver offset from the transmitter which is fixed at Station 0. Responses are typical in that a rapid falloffin amplitude is seen at short distances from the transmitter and a flat response is seen at large offsets.Three variations of the model in Figure 4.48 are considered. In the first, the depth is a 1 km square, 50 mthick, target centered beneath the origin is varied between 10 m and 3 km below sea floor (BSF). Twowater columns are used. Figure 4.50 compares results for a 100 m water column which corresponds to theproposed CarbonNet CCS project. Figure 4.51 compares results for a 1 km water column which is a morelikely scenario should CO 2 be sequestered in the Bass Strait oil reservoirs in the future. Comparison ofFigure 4.50 and 4.51 shows the benefit of a deeper water column viz. that detection is possible at depthsapproaching those of CO 2 sequestration. Results are shown only for the East component of electricfields. However, analysis of other components leads to similar conclusions.| 99

0 0 0Figure 4.49: MCSEM Example results. The underlying model was illustrated in Figure 4.48. Verticalscales on (A)-(C) are logarithmic and on (D)-(E), they are linear. The targets extents areindicated by a shaded rectangle on each plot. The effect of the target is seen in mainly inE E and B N components as a small bump in the responses at lower frequencies.100 |

Figure 4.50: Dependence of mCSEM response of target depth in 100 m water column.Solid lines plothost response while dotted lines plot target responses. In a 100 m water column, it isdifficult to detect 1 km square targets when they are much deeper than 300 m below seafloor.| 101

Figure 4.51: Dependence of mCSEM response of target depth in 1 km water column. Solid lines plothost response while dotted lines plot target responses. In a 1 km water column, it is easier todetect 1 km square targets, though targets deeper than 3 km below sea floor are problematic.102 |

The second variation of Figure 4.48 considers targets of varying volume and how to orient repeat surveysso that they clearly show differences in response which are related to the change in reservoir resistivityas conductive marine sediments are displaced by resistive CO 2 . Model responses are compared whentarget’s western most edge is fixed at 0 kmE (Figure 4.52, when the target centre is fixed at 5 kmE (Figure4.53) and when the target’s eastern most edge is fixed at 10 kmE (Figure 4.54) and all models considera 100 m water column.Table 4.6: Target size variation in mCSEM study. Reservoirs are typically modelled at 50-100 m thick.Length (m) Thickness (m) Volume (10 6 m 3 )10 000 500 50 0008000 400 25 6006400 320 13 107.25120 256 67114096 204.8 34363276.8 163.84 1759.222621.44 131.07 900.722097.15 104.86 461.171677.72 83.89 236.121342.18 67.12 120.891073.74 53.69 61.90858.99 42.95 31.69687.20 34.36 16.23549.76 27.50 8.30439.81 21.99 4.25351.84 17.59 2.17Comparison of Figures 4.52, 4.53 and 4.54 shows that only when surveys are positioned to detect variationin the reservoir’s western edge (Figure 4.54) is delineation of target edge variation possible. Eventhen, only one component (E Z ) shows reasonable variation. This suggests that mCSEM will need to becombined with other methods in order to be a viable M&V method in shallow water.| 103

0 0 0East

0 A EE Component0B E Z Component0 C BN Component

0 0 0W Edge

The third variation of Figure 4.48 repeats the second variation but for a 1 km water column. With theexception of data in Figure 4.55, where the western edge of the target is fixed and the western edgevaries, responses show greater variation that for the equivalent 100 m water column cases. The ability todelineate a target’s extent depends on the survey configuration. If surveys straddle the injection well, andthe CO 2 plume expands symmetrically around that well, then mCSEM can only delineate targets whentheir length is greater than 4 km (Figure 4.56). However, if surveys are positioned some distance fromthe injection well so that in plume moves towards the transmitter, as in Figure 4.57, then as with a 100 mwater column in Figure 4.53, variations in target size can clearly be distinguished. The advantage of adeeper water column is that more confidence can be ascribed to an interpretation because all measuredcomponents show similar variation.| 107

0 0 0East

0 A EE Component0B E Z Component0 C BN Component

0 0 0W Edge

4.2.4 Ground electromagneticsThe mCSEM method seen in Section 4.2.3 has a land analogue. The Long Offset Transient Electromagnetic(LOTEM) system was designed by Strack and others (Strack, 1984, 2010) in the mid-1980’s.In contrast to electromagnetic surveys in Sections 4.2.2.1 and 4.2.2.2, the LOTEM system operates inthe time domain. LOTEM Systems have been used in land hydrocarbon exploration (Strack and Vozoff,1996) and to monitor fluid injection in petroleum reservoirs (Ceia et al., 2007).LOTEM is a philosophy rather than an off-the-shelf system. It is a particular implementation at a particularsite rather than a general system that can be applied everywhere. The advantage of this is clearlythat system parameters such as transmitter waveform and separation as well as measurement gates canbe optimised for a particular site. For example, many different transmitter waveforms, such as 50 and100% duty cycle square waves and pseudo-random binary sequences, have been used, with different basefrequencies; receiver gates are also in general, different between surveys. A disadvantage is that modellinggeneric LOTEM systems is not possible. Nevertheless, LOTEM surveys have common features(e.g. Figure 4.58), and these are• one or more electric dipole transmitters in different orientations, located far from the receivers• electric and magnetic field receiversThe aim of the modelling in this section is to investigate sensitivity of a LOTEM system to resistive structuresof various sizes at depth rather than the applicability of LOTEM in a SW Hub environment. As withmodelling in Section 4.2.3, the rectilinear prisms used to model CO 2 plumes are gross simplifications.After establishing fundamental geology at the SW Hub using modern data, more modelling is requiredto optimise system parameters for this environment. Accordingly, a generic system, consisting of a 50%duty cycle 0.25 Hz square-wave transmitter waveform with a peak current of 10 A was modelled. Gatesfor this system are listed in Table 4.7. As changes in the model’s electrical resistivity are important, thissection concentrates on electric field responses. It is important to note that field deployment of a LOTEMsystem will include other components of other fields. These components contain a wealth of informationabout the spatial distribution of the target. However, for the simplified models examined here, symmetryreduces the response of most of these components to zero; for an exploratory study, it suffices to examinethe east component of the electrical field.| 111

NRx100 300 mTx electric dipole 12 km2 15 kmRxRx100 300 mRxRxFigure 4.58: LOTEM Survey schematic. Receivers can be either electric or magnetic dipoles whichmeasure electric and magnetic fields respectively. Fields are denoted East (E), North (N)and Vertical (Z) for fields oriented parallel to the appropriate direction so E Z refers to theelectric field measured in the vertical direction.112 |

Table 4.7: LOTEM Gate parameters. These were designed for efficient use of the offtime rather thanfor a particular site. Application of LOTEM prospecting at a specific site would likely usedifferent parameters. All times are in milliseconds.Open Centre Close1 0.056 0.078 0.12 0.1 0.139 0.1783 0.178 0.247 0.3164 0.316 0.439 0.5625 0.562 0.781 1.06 1.0 1.39 1.787 1.78 2.47 3.168 3.16 4.39 5.629 5.62 7.81 10.10 10. 13.9 17.811 1.78 24.7 31.612 3.16 43.9 56.213 5.62 78.1 100.14 100. 139. 178.15 178. 247. 316.16 316. 439. 562.17 562. 781. 1000.| 113

Modelling in this section is designed to establish the general applicability of a LOTEM system to CO 2M&V. Dependence of response upon target size, depth and resistivity is examined. It is expected thatsuch a system will be used in time-lapse mode, so that results are presented as ratios to backgroundresponses. Clearly, such an approach requires low-noise data to be effective. It is also important tonote that taking ratios obscures response amplitudes. For some environments, specfically, deep targets inhighly-resistive regimes, response amplitudes may be so close to the noise floor to reduce the method’seffectiveness to zero.Figure 4.59 plots the variation of LOTEM E E response as the the size of a 50 Ωm target is varied from2500 to 3000 m in a 1000 Ωm halfspace at selected times over the decay range. Figure 4.59 clearly showsthat a LOTEM system measuring E E can distinguish variations in target size once targets are larger than1500×1500 m. Figure 4.59 also shows that some degree of system tuning might be required in order todetect changes in plume depth.Figure 4.60 plots the variation of LOTEM E E response as the depth-to-top of a 1500×1500×100 m50 Ωm target is varied from 500 to 3000 m in a 1000 Ωm halfspace at selected times over the decay range.Figure 4.60 suggests that some degree of system tuning, experimentation with transmitter waveformfrequency and receiver gates is required to optimise a LOTEM system for detection of CO 2 plumes atthe depths of interest.The final, brief, examination of the applicability of LOTEM to CO 2 M&V looks at changes in responsewith changes in target resistivity. Figure 4.61 plots the variation of LOTEM E E response as target resistivityis varied from 250 to 7.8 Ωm. As might be expected from the underlying physics, EM methodsrespond better to conductors than to resistors, and targets become more difficult to detect as they becomemore resistive, especially in resistive regimes. This means that LOTEM methods will require structuralconstraints in order to correctly resolve changes in resistivity to be effective as an M&V technique.114 |

Response Background1.0101.0051.0000.995Target size mBackground25002000150010007505004.39285Response Background0.9901.0101.0051.0000.9952000 4000 6000 8000 10 000 12 000 14 000Target size mBackground2500200015001000750500Station m(a) 4.39 ms43.9285Response Background0.9901.0101.0051.0000.9952000 4000 6000 8000 10 000 12 000 14 000Target size mBackground2500200015001000750500Station m(b) 43.9 ms439.2850.9902000 4000 6000 8000 10 000 12 000 14 000Station m(c) 439 msFigure 4.59: Variation of LOTEM response with target size. Figures a, b, and c compare the ratio ofLOTEM E E responses to background response at 4.39, 43.9 and 439 ms respectively. Responsefalloff for smaller targets, though some discrimination of target size is possible.Targets are centred over 7500 m. The feature at 13 500 m in Figure c results from takingratios over amplitude crossovers.| 115

1.010Time:4.39 msResponse Background1.0051.0000.995Background500 m1000 m1500 m2000 m2500 m3000 mResponse Background0.9901.0101.0051.0000.9952000 4000 6000 8000 10 000 12 000 14 000Station m(a) 1.13 msTime: 43.93 msBackground500 m1000 m1500 m2000 m2500 m3000 mResponse Background0.9901.0101.0051.0000.9952000 4000 6000 8000 10 000 12 000 14 000Station mBackground500 m1000 m1500 m2000 m2500 m3000 m(b) 11.29 msTime: 439.28 ms0.9902000 4000 6000 8000 10 000 12 000 14 000Station m(c) 112.95 msFigure 4.60: Variation of LOTEM response with target depth. Figures a, b, and c compare LOTEM E Eresponse at 4.39, 43.9 and 439 ms respectively. LOTEM discrimination of target depth isseen to improve with deeper targets. This is more a function of the combination of modelledsystem parameters and the model of a single target in a resistive halfspace than any generalrule. The target’s lateral extents are shown by grey shading. The feature at 13 500 in Figurec results from taking ratios over amplitude crossovers.116 |

Response Background1.101.051.000.95Target resistivity mBackground250125.62.531.2515.6257.81254.39285Response Background0.901.101.051.000.952000 4000 6000 8000 10 000 12 000 14 000Target resistivity mBackground250125.62.531.2515.6257.8125Station m(a) 1.13 ms43.9285Response Background0.901.101.051.000.952000 4000 6000 8000 10 000 12 000 14 000Station mTarget resistivity mBackground250125.62.531.2515.6257.8125(b) 11.29 ms439.2850.902000 4000 6000 8000 10 000 12 000 14 000Station m(c) 112.95 msFigure 4.61: Variation of LOTEM response with target resistivity. Figures a, b, and c compare LOTEME E response at 4.39, 43.9 and 439 ms respectively. In general, targets are easier to detectthe more conductive they are. For CCS M&V, this means the increase in CO 2 saturationwill be accompanied by a decrease in LOTEM response. The feature at 13 500 in Figure cresults from taking ratios over amplitude crossovers.| 117

4.3 Gravimetric surveyingDisplacement of formation water by injected CO 2 changes the aquifer’s bulk density and gravimetricsurveys are sensitive to this density change. Neglecting the change due to CO 2 dissolution in formationwater, the change in bulk density can be calculated (Gasperikova and Hoversten, 2008) asD bulk = (1 − S Brine − S CO2 )D Grain + S Brine D Brine + S CO2 D CO2 (4.4)where S is saturation, D is density and grain density depends on the host rock. Measurements by Yanet al. (2011) suggest that formation water density will increase only if apparent mass density of CO 2 information water is greater than formation water density at same conditions and it is possible for formationwater density to decrease as a result of mixing with CO 2 . Their results were somewhat confirmed by Ekeet al. (2011) who suggest a 0.2% increase in saline ground water density as it is saturated with CO 2 .Typical values for sandstone and shale densities are given in Table 4.8.Table 4.8: Typical bulk densities of aquifer rocks. Generally, formation water and gas decrease thedensity of sandstone.Type Density ( kg/m 3 ) CommentsSandstone 2650Sandstone (wet) 2500 porosity of 0.1Sandstone (gas) 2320 porosity of 0.2Shale 2000 – 2800Gravity surveys may be carried out from the air, the surface (land or sea) or underground. Airbornegravity surveys are typically carried out as a regional mapping tool. Regional gravity survey results forthe SW Hub were presented in Figure 2.5 and show only broad, basement features.Gravity measurements suffer an r –2 falloff with distance from source. Intuitively, surface measurementsare not expected to be successful, especially considering sequestration depths below 800 m. Surfacemeasurements will also suffer in low permeability / low porosity aquifers where very large quantitiesof CO 2 are required for an appreciable density contrast. Because of the very low (

Primary Industries, 2011). However, at the time of writing, it is unclear what data (e.g. total field, vectoror tensor) were collected or when they would be made publicly available.Gravity response is illustrated in reference to the model in Figure 4.62. To simulate the effect of interbeddedshales which form the SW Hub’s trapping mechanism, a number of differently-sized rectangularprisms, ranging in size from 137×137 to 965×965 m and in thickness from 1 to 24 m were located (randomly)over a 379×379×39 m reservoir which is located 1500 m below the origin. The surface responseof this model is illustrated in Figure 4.63. This model has a small response which covers some 15 mGals.However, when reservoir density is lowered to simulate CO 2 injection, the resulting response is identicalto that in Figure 4.63 indicating that the response in Figure 4.63 can be attributed to the combined effectof the prisms which overly the reservoir. In this case, the time-lapse effect cannot be seen in surfacesurveys.Downhole surveys must be used to detect the reservoir change from CO 2 injection. Figure 4.64 illustratesthe response in drill holes indicated in Figure 4.62. Vertical drill holes DDH1 and DDH2 are at Eastingsof 100 and 400 m respectively. Because of its proximity, density change in the reservoir is most clearlyseen in DDH1. For the same reasons, the time-lapse response is also much larger in the closer DDH1than in DDH2.Figure 4.64 shows clearly that gravity can be used for CO 2 M&V provided that the response is measuredover several vintages and downhole. Because of the R 2 falloff suffered by the method (R 3 when gradientsare measured), based on Figures 4.63 and 4.64, distances between the CO 2 plume and the measurementdrill hole should be < 500 m.| 119

0Gravity model1000RL20001000500East 050010003000 1000 500 0 500 1000NorthFigure 4.62: Model used to illustrate gravity response of CO 2 . Prisms are rectilinear and range insize from 137×137 to 965×965 m and in thickness from 1 to 24 m. A single reservoir379×379×39 m is located 1500 m beneath the origin. Drill holes are indicated by the orange(DDH1) and magenta (DDH2) lines and are 100 and 400 m directly east of the origin.Figure 4.63: Surface gravity response from the model in Figure 4.62. This response is due more to thecombined effect of the prisms located above the reservoir than the reservoir. Simulationof a time-lapse survey shows that the time-lapse response is essentially zero echoing theresults of Sherlock et al. (2006) that surface gravity surveys for CO 2 M&V are possibleonly in the most favourable of circumstances.120 |

Effect of drill hole offset0DDH10DDH20Time lapse 500 500 500100010001000RL m1500RL m1500RL m1500 2000 2000 2000 2500 2500 2500 3000100 50 0 50 100 150 200Response mGals 3000100 50 0 50Response mGals 300010 5 0 5 10Response mGalsFigure 4.64: Downhole gravity response from the model in Figure 4.62. Dashed profiles indicate baselinevintages. Both drill holes are vertical. DDH 1 is 100 m East of the origin while DDH2 is 400 m Drillholes were plotted in Figure 4.62.| 121

4.4 Other methods4.4.1 Passive seismic monitoringInjection of CO 2 into the subsurface will alter the pore pressure inside and around a reservoir. Changes inpore pressure can lead to reactivation of pre-existing faults, the formation of new fault fracture networksand the movements of fluids. These processes in the subsurface can emit seismic energy. Locating andanalysing the so called microseismicity allows us to identify the geomechanical deformation induced byinjection (e.g. Verdon et al., 2009). Clearly, in the case of a CO 2 sequestration project, the goal is to avoidany geomechanical deformation during the injection as this could affect the integrity of potential seals,activate pre-existing faults or lead to the formation of new faults or fractures. It is therefore important tomonitor the microseismicity during the injection of CO 2 .Table 4.9 gives an overview of the various location techniques that have been proposed and used to locatemicroseismicity. Geiger (1910, 1912) solved the non linear problem of earthquake location given a setof travel time observations using an iterative non linear least square algorithm and a velocity model.Time reverse imaging is based on the idea of wavefield-reconstruction backward in time followed bylocating the source using an imaging condition. (e.g. Gajewski and Tessmer, 2005; Sava, 2011). Theadvantage of time reverse imaging is that there is no need to identify arrivals. Uncertainties in the velocitymodel limit the location accuracy of the aforementioned absolute location techniques. Relative locationtechniques have the advantage that uncertainties in the velocity model do not play a role as long as itis possible to locate a few key events with a high accuracy. The double difference tomography method(e.g. Waldhauser and Ellsworth, 2000; Zhou et al., 2010) has been widely used to invert for a velocitymodel and locate the events at the same time. Double difference locations require measurements of Pand S arrival times for each event that has to be located. In a microseismicity context the goal is to locateseveral thousand events, which means that several thousand arrivals have to be identified. If coda waveinterferometry (e.g. Snieder and Vrijlandt, 2005) is used to obtain relative locations, one only has toidentify the coda on the recordings of the individual events. Passive seismic methods can also be used toderive structural models either through joint inversion for a velocity model and the event locations or byanalysing the ambient seismic noise.Table 4.9: Overview of the commonly used microseismicity location techniquesTravel time based Waveform basedabsolute locations least square time-reverse imagingrelative location double difference coda wave interferometryAmbient noise and microseismicity recorded for a reservoir contains information about the subsurface.Saenger et al. (2009) for example analysed spectral anomalies of passive low-frequency seismic noiseand found a correlation between the anomalies and hydrocarbon accumulations. More recently, Xu et al.(2012) extracted surface waves and reflected waves from ambient seismic noise by seismic interferometryused them to image the subsurface at the Ketzin experimental CO 2 storage site.122 |

Microseismic monitoring ideally begins pre-injection to establish a baseline for the microseismicity andthe ambient seismic noise. Currently, passive seismic methods are unlikely to have the same spatialaccuracy as controlled source seismic or electromagnetic methods with respect to locating a plume.However, the advantage of passive seismic techniques is that once the infrastructure is in place theyallow for continuous monitoring of the site with minimal impact.| 123

4.4.2 Nuclear Magnetic ResonanceThe nuclear magnetic resonance (NMR) technique is a geophysical method that has long been usedin the petroleum industry (Hirasaki et al., 2003) where it is typically used to determine porosity andpermeability as well as for hydrocarbon typing (Coates et al., 1999). The method has had recent successin determining water quality and mapping near-surface (Meju et al., 2002) aquifers. As a result of theseprior uses and successes, the method may have applications to mapping CO 2 distribution in either salineaquifers or depleted hydrocarbon reservoirs. NMR surveys for water quality may be carried out eitherfrom the surface or downhole whereas petroleum applications are almost exclusively downhole. If NMRis to be applied in CO 2 M&V, then it is also likely to be through borehole applications. Completedescription of NMR logging is well-covered by other authors (e.g. Coates et al., 1999; Stapf and Han,2006) and beyond the scope of this report, though a brief description follows.NMR measurements can be made on any nucleus with an odd number of neutrons or protons or both (Coateset al., 1999). Examples of such nuclei include 1 H, 13 C and 23 Na. NMR measures the response of atomicnuclei to applied magnetic fields. Many nuclei have a net magnetic moment and spin and their interactionwith an external magnetic field can be measured. When an external magnetic field B 0 is applied, suitablenuclei precess longitudinally so that the precession axis is parallel to B 0 and a net (bulk) magnetisationM 0 grows exponentially. The net magnetisation is defined asM 0 = NB 0γ 2 h 2 I(I + 1)3(4π 2 )kT(4.5)where B 0 is a static magnetic field, I is the net spin, h is Plank’s constant, γ is the gyromagnetic ratio, kis the Boltzmann constant, N is the number of nuclei per unit volume and T is the absolute temperature.The time taken for the M 0 to reach 63% of its final value is termed T 1 . The net magnetisation is thentipped into the transverse plane by applying an oscillating magnetic field B 1 . When B 1 is turned off, thetipped net magnetisation decays exponentially with time constant T 2 . Measurement of this decay givesinformation about petrophysical characteristics of the rock. For tipping to be effective, B 1 must oscillateat the Larmor frequency f relative to B 0 . The Larmor frequency is definedf = γ B 02π(4.6)The gyromagnetic ratio, γ, is a measure of the strength of nuclear magnetisation. For 1 H, γ is 267.54MHz/T. Direct NMR measurements of 13 C can be made (Diefenbacher et al., 2011), and such measurementscould lead to a direct estimation of CO 2 saturation, but γ for 13 C is 66.73 MHz/T, around ¼ that of1 H, making these measurements more difficult. Accordingly, the most appropriate application of NMRfor CO 2 is likely to be where 1 H is measured in some form as formation water or residual gas displacedby CO 2 (Hussain et al., 2011). Clearly, this requires multiple surveys over the sequestration project’slifecycle.NMR samples much less of the environment than other geophysical techniques discussed in this report.The depth of investigation of an NMR tool increases with decreasing B 1 frequency (Coates et al., 1999)and increasing the depth of investigation lowers the SN ratio. The field B 0 is typically generated by a124 |

ferromagnetic magnet which has a temperature-dependant magnetisation. With increasing temperatures,B 0 decreases and therefore depth of investigation decreases. The dependence of depth of investigationon B 1 frequency and temperature is plotted in Figure 4.65 for a 6 in tool.Diameter (in.)2019181716151450°F100°F150°F200°F250°F300°F1312500 550 600 650 700 750 800 850 900 950 1,000Frequency (kHz)om000884Figure 4.65: NMR Depth of investigation dependency on B 1 frequency and temperature for a6 in tool (after Coates et al., 1999). The depth of investigation of a 4 ½ in tool is just over70% of values plotted. As is typical in the petroleum industry, imperial units are used.Rajan et al. (1975) have shown that the presence of CO 2 in mixtures with methane results in a reduction ofthe relaxation time compared to the correlation time for pure methane. Hirasaki et al. (2003) suggest thatif correlation is against molar density instead of mass density as is typical, then methane-CO 2 mixturerelaxation times will approximately correlate with those of methane. This is shown in Figure 4.66. For aspherical molecule, the longitudinal relaxation T 1 in Figure 4.66 is defined as1= 40πM 2a 3 ηT 1 9kT(4.7)where a is the radius of a spherical molecule, η is the viscosity and other quantities were defined inEquation 4.5. For a diatomic molecule, the coefficient M 2 is definedM 2 = 9 20( )2µ0 ¯h 2 γ 44π r 6where µ 0 is the permeability of free space, ¯h is Plank’s constant (divided by 2 π) and r is distance betweenthe nearest protons in the molecule.Since there is a correlation between NMR response and methane-CO 2 mixture mass density, furtherresearch into the use of NMR as a monitoring tool in CCS is recommended. However, initial successof NMR in CO 2 M&V is likely to be in measuring the change in pore fluids as they displaced by CO 2 .Indeed, this was a central conclusion of Grombacher et al. (2012)’s study.| 125

Figure 4.66: NMR relaxation time T 1 of methane in the presence of CO 2 as a function of temperature(after Rajan et al., 1975). Mixture relaxation times approximately correlate with methanerelaxation times suggesting that NMR might be used in CO 2 M&V.126 |

5 Monitoring & verification strategiesThe Offshore Petroleum and Greenhouse Gas Storage Act 2006 (with amendments) (Department ofResources Energy and Tourism, 2011b) provides the minimum monitoring and verification requirementsthat must be met by the operator of an offshore CO 2 sequestration site. Offshore geosequestration sitesare regulated by Federal laws while onshore sites will be regulated by State laws. Central to the Actis that the reservoir has to behave as predicted in the site plan submitted by the operator. A forwardmodel is required for all plume migration scenarios for which the probability of occurrence is greaterthan 10%. Currently, the monitoring plan has to be designed in such a way that it allows verificationof plume migration predictions. Further to this an operator will have to provide a comprehensive riskassessment plan covering all risks that could conceivably arise from the operation of the site.A key requirement for a comprehensive risk assessment are quantitative interpretations of monitoringsurveys that account for uncertainties. Considering the seismic studies undertaken in this work, only theBayesian approach employed in Section 4.1.3 allows us to, for example, comprehensively assess the riskthat seismic monitoring will not detect the plume migrating along a different pathway.Common to all the geophysical remote sensing tools used as part of a monitoring framework is that onlytime lapse surveys will provide the required detectability. Figure 5.1 illustrates this for a single boreholeEM survey. Structural and, in particular, lithological uncertainties make it impossible to reliably detectthe presence of CO 2 without the use of time lapse surveys. The success of time lapse surveys dependson the quality of a baseline survey. It is therefore important to establish baseline surveys before theinjection of CO 2 . This is not limited to active source methods, it includes ambient noise and microseismicmonitoring frameworks. Changes in the ambient noise field can only be used to locate a plume if thebackground ambient noise field is known.Qualitative interpretation of time lapse surveys commonly requires the quality of the individual vintagesto be degraded to the quality of the poorest survey. Bayesian approaches for the quantitative interpretationof geophysical data can use different vintages of data with different quality without the need to degradethe quality of the better vintage. This is particularly important, as over the lifetime of a reservoir, one canexpect seismic acquisition technology to significantly evolve and variations in the near surface conditionto influence individual surveys differently.The goal of a monitoring survey will be to determine the distribution of CO 2 saturation in the subsurfaceto a given confidence level. Individual monitoring techniques will achieve different levels of confidenceand combining different techniques will increase the joint confidence. Passive seismic methods and wellmonitoring are likely to be undertaken continuously over the lifetime of the reservoir. However, they arenot sufficient to by themselves locate a plume. Airborne gravity data can be collected over the reservoirwith little impact and might help to constrain a plume’s dimensions given a well constrained reservoirstructure and the use of a time lapse approach. Reaching confidence levels for the inferred plume locationof at least 95% will require seismic and, most likely, cross borehole EM time lapse surveys. Clearly, amonitoring and verification program will have to map out the extent of the plume, which calls for atleast several 2D surveys, and ideally a 3D survey. If the goal is to determine a saturation distribution,then seismic surveys will be sensitive at lower saturation values while EM surveys are sensitive at higher| 127

saturation values. This suggests that seismic methods could be used near the probable edge of a plume,while EM methods could be used over the centre of the plume. Gravity surveys might also be used toplace bounds on the plume.a)0horizontal distance (km)9 10 11b)0vertical quadrature response (PPM)−30 −25 −20 −15 −10c)0vertical quadrature response (PPM)−0.2 0 0.2 0.4 0.6 0.8−50030% CO 2 saturationNo CO2−500elevation (km)−1MyallupWonnerupBoreholedepth (m)−1000−1500depth (m)−1000−1500net to gross1.000.950.90−20.85−2000−20000.800.750.700.65−2500−2500Figure 5.1: Illustration of uncertainty in the depth to CO 2 migration front. Figure (a) shows a singlerealisation of the conceptual model around the borehole which is denoted by a dashed line.Figure (b) compares vertical quadrature response in PPM at 10 Hz for 30% CO 2 and for noCO 2 . Figure (c) plots the difference in the vertical quadrature response in PPM at 10 Hzbetween a saturation of 30% CO 2 and no CO 2 . Some 100 independent model realisationswere randomly generated in order to compile Figures (b) and (c). The different depths to thetop of the CO 2 in Figure (c) result from a combination of model discretisation and solutionsamplingscheme. This figure demonstrates the efficacy of using electromagnetics to monitorchanges in CO 2 disposition.128 |

5.1 General recommendations & strategies for CCS projectsIt is important to keep in mind that specific monitoring and verification frameworks will be site dependent.As CO 2 reacts with the formation water and minerals in the reservoir its detectability is likely tochange over the life of the CCS project. Benson and Cole (2008) propose basic and enhanced monitoringprograms, modified versions of which are reproduced in Table 5.1. This study recommends that geophysicalmethods be undertaken as part of a basic program rather than (for the most part) an advancedone as Benson and Cole (2008) suggest and we have modified their original table accordingly. One ofthe particular lessons of this report is that models require proper geological calibration, and this can reallyonly be achieved using extant well logs. Another omission from Table 5.1 is noise measurements.This report has shown that these are critical to establishing CO 2 detectability levels. Noise measurementsform an important component of baseline studies. Table 5.1 includes a number of monitoring techniques,all of which can be used together. Particularly worthy of further investigation is the possibility of usinggeochemical measurements to calibrate geophysical inversion results. NMR Measurements can also beused to calibrate geophysical inversions.Table 5.1: Two general M&V programs (modified from Benson and Cole, 2008). The central modificationis to move geophysical techniques from advanced to basic programs. This table includesa number of monitoring techniques so geophysical methods are italicised for emphasis. Geophysicalmethods also have a role to play in monitoring water quality. Geochemical M&Vtechniques might be used to provide spot calibration of geophysical inversion results.Basicseismics, well logs, gravity, EM, wellheadpressure, formation pressure, injection andproduction rate testing, atmosphericBasicMicroseismics, seismics, well logs, gravity,EM, wellhead pressure, injection and productionrates, wellhead atmospheric CO 2 monitoringBasicseismics, gravity, EMPre-operational monitoring (Baseline studies)AdvancedOperational monitoring+ CO 2 flux monitoring, pressure and waterquality above the storage formationAdvancedPost-operational monitoring+ continuous CO 2 flux monitoring, pressureand water quality above the storage formationAdvanced+ continuous CO 2 flux monitoring, pressureand water quality above the storage formation,wellhead pressureThe monitoring program(s) in Table 5.1 must be considered in conjunction with cost. Table 5.2 comparesthe relative costs of various geophysical M&V methods relative to surface 3D seismics. In general, costsare low (relative to surface 3D seismics) though there is a significant caveat. EM and gravity methodsshould generally be used downhole. If vertical holes are too close to the injection horizon, then thereis the possibility of CO 2 plumes moving past the drill hole, significantly complicating interpretation.| 129

However, if vertical holes are placed too far away, then signals are too small to be useful. Multipleholes might reasonably be used, but CO 2 sequestration must take place at depths below 800 m and deepholes are expensive. A compromise might be to use horizontal holes for downhole monitoring, butdeep horizontal holes are also expensive. Another caveat is with the use of permanent seismic arrays.Whether source, receiver or both are installed, costs can be significant. However, these costs can beamortised over the cost of the project and in this light, the cost of permanent seismic installations arelow. A further benefit is that permanent installed arrays can significantly reduce the impact of repeatsurveys on the community.130 |

Table 5.2: Suitability of geophysical methods for CO 2 monitoring and verification. Cost is relative to 3D surface seismic surveys which are high. All surveysare required to be time-lapse surveys.Method Section Rel.costComments Advantages DisadvantagesSurface seismics 4.1 unity 3D Surveys are required to characterise structure, although2D surveys appear adequate for monitoring.High resolution; gooddepth penetrationEffectiveness diminishesat higher CO 2 saturations.Permanent arrayseismics4.1.6.1 low Initial cost is high, however gives significant savingswith each repeat survey. Significantly improves repeatabilityand CO 2 detectability.High resolution; significantimprovement in S/NratioHigh initial cost.Microseismics 4.4.1 low Lag between CO 2 injection and microseismics eventmakes method difficult to apply for M&V. Thereshould be a decline in microseismics activity overtime indicating equilibrium.Continuous reservoirmonitoringRequires planning beforeinstallation.EM 4.2 low Requires multiple downhole surveys on land. Inmarine environments, deeper water columns are requiredfor success. Cost increases rapidly as moredrill holes are used.Good discrimination ofsaturation at high saturationLow resolution withoutconstraints; large signalfalloff with increasingdepths.Gravity 4.3 low Limited use of surface surveys; downhole instrumentsrequire considerable development. Cost increasesrapidly as more drill holes are used.Poor resolution; large signalfalloff with increasingdepths.NMR 4.4.2 low More research is required, but promising becauseof NMR’s potential to give direct estimates of theamount of H 2 O that has been displaced by CO 2 andalso a direct measure of the amount of CO 2 if NMRis tuned to measure 13 C rather than 1 H.Opportunity to directlyinterpret CO 2 saturation;permeability estimatesPoor depth penetrationbeyond drillhole.| 131

5.2 Recommendations for flagship CCS projectsIn general, recommendations of M&V programs for the two flagship CCS projects considered in thisreport differ only in the reconfiguration of surveys between the land operations at SW Hub and marineoperations at CarbonNet. Programs are considered in terms of pre-injection, injection and post-injectionphases of the project. The intent with such programs is to acquire appropriate data to confirm modellingresults since CPU time is usually cheaper than collecting field data, thus maximally utilising modelling’spredictive power. It is acknowledged that this depends somewhat on the modelling performed. Invertingany production dataset for a 3D CO 2 distribution is expected to require substantial computing resourcesfor the next few years at least.It is not possible to specify survey requirements for any method at either flagship project beyond generalities.Neither project has advanced to the point where a high-quality 3D geological model has beenestablished. Since neither project has advanced sufficiently, modelling of CO 2 flow paths is yet to becompleted. The nature of these flow paths will lead to specific survey parameters for particular methodsat individual storage sites. At the current stage of both projects, the best that modelling can do is offerguidelines as to the general applicability of a method. A example of this is in Section 4.2.3, where modellinghas shown that the marine CSEM method is sub-optimal for CO 2 M&V in water columns of lessthan 100 m because the CO 2 is required to be too close to the seafloor to maintain supercriticality. Withsuch a caveat in mind, recommendations for the geophysical M&V programs for both flagship projectsare given, based on project phase.In the pre-injection phase, the goal is to establish a baseline survey for any monitoring technique thatwill be employed later in the project. It is essential at this stage to construct the best possible reservoirmodel. This requires appropriate well logs as well as optimised 3D seismic surveys. It is also essentialto characterise the background noise for particular methods. It is at this stage that permanent seismicarrays should be installed. If the decision is made to use a ground or marine EM system, then extensivemodelling will be required in order to tune the waveform (or frequencies) to optimise the system for theparticular project.The injection phase of the project is characterised by mapping plume movement and monitoring injectionrates to avoid rock fracture. Microseismics have a role to play here, since they can provide a real-timeestimate of reservoir deformation. Other methods, such as seismics, EM and gravity, can be used tomap the plume. Both EM and seismic methods are essentially volumetric measures so that their efficacyis directly dependent on porosity. Both methods will be more effective in high porosity regimes suchas Gippsland, than in low porosity regimes such as SW Hub. For example, Section 4.1.3 showed thatseismics were sensitive to low CO 2 saturations while Section 4.2.2.1 shows that EM was more sensitiveto high saturations. If a permanent seismic array is installed, then cost-effective high-quality time-lapsesurveys can be made at any time during the injection phase.The post-injection phase of the CCS project is concerned with monitoring the injected CO 2 plume toensure that it remains in the intended reservoir. Special care must be taken in the longer term not toconfuse seepage with trapping, since the effectiveness of some trapping mechanisms could easily be132 |

confused with seepage. In this case, multiple measurements of physical properties are required, possiblyin conjunction with geochemistry, to evaluate scenarios probabilistically.With these comments in mind, and recognising the considerable amount work remaining that is neededin order to adequately characterise the SW Hub and CarbonNet CCS projects, the following costs and’back-of-envelope’ survey parameters are estimated.Surface seismic surveys are roughly estimated as between $50 000 for a 1 km 2 survey. Specific CO 2amounts that could be detected in a low-porosity environment such as the SW Hub depend on injectiondepth and the S/N ratio that is achieved, but Section 4.1.4.2 showed that amounts of 40 000 t were notunreasonable.Similar caveats regarding porosity, injection depth and S/N ratio achieved, apply for marine seismicsurveys. However, in the low noise marine environment, where much larger S/N ratios can be achieved,modelling in Section 4.1.4.3 showed that plumes as small as 3000 t could be detected in a high-porosityenvironment such as the Latrobe Formation in the CarbonNet conceptual model. Costs of marine seismicsurveys are difficult to estimate because of their reliance on ship charter.The cost of a 500 m instrumented CO 2 drillhole is roughly around $150 000. Significant savings can beachieved for large-scale deployments.Permanent seismic arrays were suggested in Section 4.1.6 as means by which S/N ratios could be improved.Costs for marine OBC installation is largely a function of ship charter and ROV piloting costs.The significant benefits of permanently-installed seismic arrays are seen in the size of CO 2 plumes thatmight be detected. These are significantly smaller than surface surveys at 600 t and 2000 t for OBC andVSP, respectively. Wells for VSP may also be used to microseismic surveys, which leads to negligiblecosts of such surveys.Electromagnetic surveying equipment was recently costed at around $500 000 for a single downholesystem. This cost includes a generator, waveform transmitter and controller, receiver unit, receiver probe,a winch capable of 2 km depths and minor development of a transmitter dipole. Such a system couldbe moved between any monitoring drill holes cased using non-conductive material with a modicum ofease. Such a system is also easily adapted to LOTEM surveying with the addition of B-field probes andconnecting wire. Commercial downhole logging survey costs are of the order of $300 000, but include asingle repeat survey.Given the considerable time needed to develop downhole NMR and gravity probes capable of low-noiseoperation at typical sequestration depths, it is not possible to estimate costs for such surveys.| 133

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6 ConclusionsThis report has discussed the ANLEC R&D project 3-0510-0030 which is entitled “A deployment strategyfor effective geophysical remote sensing of CO 2 sequestration”. The objectives of this project were:-• To develop conceptual reservoir models which span the likely geometries and performance of thepotential demonstration flagships;• To forward model possible physical measurements;• To understand the sensitivity of the measurements to CO 2 ;• To recommend the combination of geometries and physics to be used for the pilot project measurements,including notional costs; and• To recommend analysis and measurement technology that needs further development.Since most current geophysical surveying methods cannot detect CO 2 directly, it is clear that no singlegeophysical surveying method in isolation has the capability to monitor CO 2 . This means that an effectiveM&V strategy must incorporate multiple geophysical and other methods. For particular scenarios, theexact combination will vary, but such methods will generally include seismics, electromagnetics andgravity.Central conclusions of this project are that:–• Time-lapse surveys are required of all geophysical methods studied in this report. It was notpossible to infer CO 2 saturation from a single geophysical data vintage. The requirement forgeophysical time-lapse surveys is concomitant with establishing high-quality baseline models;• Extant high-quality well logging data are required to build high-quality geological models;• Accounting for uncertainties in seismic modelling improves the ability to evaluate CO 2 saturationand is required for robust risk assessment;• Permanent seismic arrays significantly improve S/N ratios allowing for cost-effective (in the longterm) acquisition of high-quality data with minimal impact to the community;• In shallow (typically < 100 m) water columns, marine electromagnetic surveys would be unlikelyto detect CO 2 variation; and• Because of the falloff in response over distance, gravity and electromagnetic surveys should beconducted downhole on land. These need not be in vertical wells.Further conclusions of this project were in the form of recommendations for future work. These arediscussed in Section 7.| 135

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7 Recommendations for future researchSeveral recommendations for future research into monitoring and verification in a CCS project followdirectly from the conclusions. These are discussed below.The research in Section 4.1 of this report suggests several avenues for for future research that couldimprove current seismic monitoring techniques. One direction of the new research is in the area oftime-lapse noise. The seismic difference section contains noise from both baseline and monitor sectionswhich results in increase of the noise compared to either of the two sections. This observation opensnew research opportunities into data processing workflows that could result in lower noise levels indifference sections. Another area of future research is the exact estimation of noise, in a time-lapsesense, for permanent receivers located in shallow wells. This estimation is closely linked to a designof the survey, which is site dependent. Finally, the issue of non-repeatable multiples in ocean bottompermanent receiver records due to changes in temperature and/or tides could be addressed by using thereceivers as virtual sources. The virtual source method (Bakulin and Calvert, 2006) is closely linked tointerferometric methods that use ambient noise for imaging.Deterministic modelling studies on specific sites at preliminary project stages, such as those in thisreport, are an efficient way to assess CO 2 detectability. However, because they are able to account foruncertainties in both data and model parameters, only stochastic methods can deliver quantitative riskestimates to projects in production and post-production phases. Due to the need to use more than onegeophysical technique, appropriate workflows and environments must be devised before commencingany CCS project.The value of modelling results is enhanced by context. The ability to run models of different geophysicalsurveys and evaluate their outputs, in a stochastic sense, for CO 2 distribution, in the context of a singlereservoir model, would significantly aid M&V using geophysical techniques. The code described bySambridge and Mosegaard (2002) is an important step in this regard. Placement of a similar code, withcomponents to model different geophysical responses, in the public domain would allow probabilisticCO 2 M&V in the longer term, without the fiscal limitations of short-term commercial entities.Models are always approximations. Sometimes, these approximations are valid, as was the case inSection 4.2.2.2, and sometimes they are particularly useful such as in Sections 4.1.3 where a large numberof models can be calculated in order that parameter space be properly sampled. However, in othersections of this report (e.g. Section 4.2.4), available programs are not capable of properly modellingthe geophysical response of CO 2 distributions. In this regard, development of fit-for-purpose modellingcodes which incorporate appropriate rock physics relationships is strongly recommended. Coupling thesewith codes which compute fluid flow would add to their realism. Placing them in the public domainwould ensure open and common modelling standards across CO 2 M&V projects over periods of timewhich exceed the average lifetime of a commercial operation.High-quality geophysical modelling of physical properties requires effective models of those physicalproperties. For acoustic methods, there are a number of effective medium theories (Berryman, 2006;Mavko et al., 2010; Gassmann, 1951) but for electromagnetic methods, the situation is not so clear. Cole-Cole (Cole and Cole, 1941) models for frequency-dependent conductivity have been used, but it is not| 137

clear how to incorporate multiphase materials within such a general framework. The GEMTIP effectivemedium model proposed by Zhdanov (2008) is a step forward, but it is not clear how to incorporategas or fluid inclusions in a model that was developed for non-sedimentary rocks. More development ofappropriate EM effective medium theories is required.As a result of the potential to directly detect CO 2 , further research into NMR methods which target the13 C isotope is recommended. As the 13 C isotope comprises 1.1% of carbon, such measurements wouldprovide useful calibration for other methods, such as reflection seismics and EM, which are effectiveover larger areas than NMR. Since NMR methods can also directly detect water, they provide a usefulindication as to how much formation water has been displaced by the injected CO 2 .Much of the rock physics in Section 3 of this report was derived from wells which were some distancefrom seismic sections which were used to derive appropriate models. This assumes that geology varieslittle over some distance. Any CCS project must use properly calibrated rock physics parameters basedon testing of representative cores. For the SW Hub, various projects will characterise recently-drilledcore and provide much-needed calibration of rock physics relationships. Undertaking a similar researchprogram for the CarbonNet flagship CCS project is recommended.Worst case scenarios for an area near geosequestered CO 2 include rapid large scale leakage of the CO 2 ,interaction of the CO 2 with aquifers used to supply drinking water and local seismicity due to change inthe stress field after injection of CO 2 . Large uncertainties in the data and in their modelling lead to variousvery different valid interpretations. The treatment of uncertainties is critical for a reliable assessment ofrisks. This is not too different from the challenges in a probabilistic seismic hazard analysis (PSHA) withthe goal of estimating the likelihood of earthquake caused ground motion at a specific site in the future.PSHAs are commonly employed when assessing the seismic hazards for nuclear power plants. The seniorseismic hazard analysis committee (Budnitz et al., 1997) outlines a detailed assessment methodology fora seismic probabilistic risk assessment. These guidelines have been the basis for recent large scaleprobabilistic seismic hazard analysis studies (e.g. the PEGASOS study in Switzerland and the YuccaMountain study in the USA). Remarkable from a CO 2 monitoring point of view is that uncertaintiesare an integral part of these studies and they distinguish between epistemic and aleatory uncertainties.Aleatory uncertainties are uncertainties that reflect the variability of the outcome of an experiment forexample a seismic survey. Epistemic uncertainties, on the other hand, are uncertainties that result froma lack of knowledge. Clearly there is currently a need for similar guidelines and methodologies to beestablished for the probabilistic assessment of risk associated with the geosequestration of CO 2 .138 |

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ACO 2 PropertiesThis Appendix gives CO 2 properties that were used in modelling of SW Hub geophysical responses.Table A.1: Rock physics parameters used for the Lesueur Group. Brine conductivity was derived fromFigure A.1 assuming NaCl formation water.Parameter Wonerup Myallup UnitsFracture pressure gradient 0.158 0.0158 MPa/m... @ 750 m 23.75 11.85 MPa... @ 90% injection pressure 21.38 10 MPaTemperature gradient 2.1 2.1 ◦ C/100 mSurface temp. 18 18 ◦ CTemp. @ 750 m 49.5 33.75 ◦ CPressure @ 750 m 21 10 MPaPermeable endmember porosity 12.9 19.7 %Net-gross 97 99 %Brine salinity 30 000 30 000 ppmBrine density 1017 1018 kg/m 3Brine V P 1605 1563 m/sCO 2 density 799 729 kg/m 3CO 2 V P 425 339 m/s| A 1

Figure A.1: Relationship between NaCL concentration, temperature and conductivity (after Schlumberger,2009). The report assumes an NaCl-dominated formation water. Clearly, accurateanalysis of formation water at a particular site is required in order to derive particular conductivities.Conversion between Table A.1 ppm and Figure units of g/L depends on manyfactors, though units of µg/l and ppm are roughly equivalent.A2|

BSeismic preprocessing workflowThe following processing flow below was applied to the seismic line 11GA_LL2 in Section 4.1.1.1:• Data load, geometry• Trace editing (manual trace kill/reverse)• Elevation statics• Predictive deconvolution• F-K filter to suppress source generated noise• Auto gain control (AGC) applied and saved (500 ms)• Forward linear moveout (LMO)• F-K domain filtering (rejection)• Inverse LMO• Remove saved AGC (500 ms)• Velocity analysis• Brute stack• Max-power auto statics• Velocity analysis• Stack• Post-stack migration• Post-processing (FX deconvolution)| B 1

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CSeismic data processing for quantitative interpretationSection 4.1.1.2 hinted at the difficulties in producing true-amplitude seismic migrated (Schleicher et al.,1993; Ekren and Ursin, 1999; Zhang et al., 2001) sections which are necessary for quantitative interpretation.This Appendix gives some explanation of these difficulties as well as the checks that can beperformed, in order to produce such sections. Reiterating from Section 4.1.1.2, there is no single perfectprocessing flow that achieves this in all situations. In addition, some workflow steps require several iterationsbefore proceeding. The remainder of this Appendix describes major components of the workflowin detail. To ease reference, Figure 4.7 is reproduced in this Appendix as Figure C.1.C.1 Near-Surface velocity correction to datumThe determination of the correct statics solution is a multi stage and cyclical process. In each step ofcorrection of the statics, a revised stack should be provided, the statistics of the statics solution examined,and if necessary, revision of previous statics must be performed. Wherever possible, uphole data shouldbe provided and compared with the velocity solution related to the statics.The considerable changes of depth to the weathering, and the significantly-varying velocity of the materialbelow weathering, in Australia’s tertiary basins usually requires the application of refractions staticsand multiple passes of residual statics. Initially, all statics should be compared with an elevation onlystatic correction that is determined from an approximate near-surface velocity estimate. This near-surfacevelocity estimate may be generated from well data, uphole data, refractions surveys or other sources. Theideal velocity for elevation statics east is that is representative of the average velocity of those materialswhose thickness changes correlate with topography. That is, if the changes in elevation are dominatedby shallow aolian sand deposits then the velocity should be that of those sands, and if the changes in elevationare associated with hard carbonates that have resisted erosion, then the elevation velocity shouldbe much higher. The elevation static can rarely be used prior to any lenient noise removal. However, itis useful for a first-past stack which can subsequently be used for quality control of subsequent staticssolutions.Well and uphole constrained refractions statics in a 3D survey, or a closely-sampled 2D survey wherecrossing lines have a tied velocity and statics solution, are the best first-pass solution. There are parts ofAustralia, in particular the Cooper Basin, where the deep reflectivity is consistent, and a very good seriesof high S/N ratio reflectors exist, where refraction statics are not necessary and a simple elevation staticfollowed by reliance on reflection statics is adequate to provide an excellent stack quality. However, inmany of these situations, where small shifts in the final seismic response may lead to a very differentappraisal of the economic potential of the structure, refractions statics are used to constrain the mediumto-longwavelength component of the statics solution.Using refractions statics on an untied or stand-alone 2D line is very likely to lead to the production offalse structures, as there is nearly always a variety of ways to interpret the refractions on a seismic surveydata. Some 3D survey designs do not lend themselves well to refraction statics calculations, particularlywhere there are a minimum of near offsets, and low fold along any receiver azimuth.| C 1

seismic input dataSPS, RPS, XPSand uphole datauphole survey dataand/or well dataspike removalmono frequencyremovalnavigation mergefold plotLMO QC − firstbreak picksnear surfacevelocity modelgain recoverydeconvolutionnon productionvelocityversion 1refraction model buildrefraction static stackelevation static stackcomparecomparecomparedeconvolutionSurface consistent amplitude compensationlinear noise removalstackresidual staticsiteration loopdemultiplevelocity repickremoval inital gain recoverydata regularisation AGC migrate version 1migrate version 2Q and gain recoveryvelocity repickresidual gain recoverystack and AVOiteration loopwell calibrationQ amplitude correctionresidual NMOFigure C.1: Schematic illustration of the workflow required to produce true-amplitude seismic sections.The abbreviations SPS, RPS and XPS are from the Shell standard (Shell InternationalePetroleum Maatschappij, B.V, 1995) for land navigational data and respectively, are ShotPositioning, Receiver Positioning and a link indicating which receivers were used in whichchannel for each shot. This Figure is a reproduction of Figure 4.7 for ease of reference.C 2 |

Preprocessing prior to any stack will require some form of gain control, shot editing, navigation merge,and de-spiking of the data. Usually, Vibroseis data has been correlated in the field. However, when botha measured and target sweep have been recorded, and the uncorrelated field records also provided, it isrecommended to compare correlation with both sweeps when a single vibe is used.Once elevation and refractions statics have been determined, the data must be re-examined with normal(NMO) and linear move out (LMO) applied. It is important to determine if the noise sources evident onthe data are from deep sources who will see the entire statics solution on a linear noise train, or a shallowrefractor who may actually be above the base of weathering. The estimation of source-generated noiseand its associated removal, preferably by adaptive subtraction, should be attempted using a number ofdifferent statics solutions.Once a preliminary statics solution has been made, and a complimentary linear noise removal methoddetermined, residual reflection statics should be estimated, preferably incorporating a target window thatcomprises strong consistent reflectors and the target strata. Testing should be performed to see the totalmagnitude of static shift allowed before cycle skipping is evident, and after each residual statics solution aresidual pass of velocity analysis can be performed. Once the total static, comprising elevation refractionand residual statics, has been determined, the processor should go back and re-examine the linear noisetests in light of this total static. It should be noted that the residual statics will be affected by processinglenient noise trains with techniques that may be the focus of the residual velocity determination. Anexample of this is some F-K filter which blur the data. This has the effect of blurring residual statics sothat the final seismic image suffers from a loss of character at faults.C.2 Linear noise filteringIn many areas, linear noise filtering can be dispensed with by the selective use of a near mute to removeground roll and a far mute to remove refraction and guided wave energy. However, this is almost neverthe case in the Australian Tertiary Environment.Sometimes, heavy 3D noise filters are required to get rid of noise trains in poorly sampled 3D datasets.Each processing software package has its different way of determining what is noise, but they all havea common process of removing the noise by adaptive subtraction. This is the best solution in that itgenerally does not smear the statics solution significantly.The very slowest velocity noise trains such as ground roll may be effectively removed by F-K or velocityfilters on 2D datasets, where there is even receiver station sampling. Such a process should be testedwith, and without static corrections prior to the process. Also, there should be a frequency limit to theeffect of such a filter, such that it does not modify subsequent residual statics determination.High incident angle reflection data is often very difficult to image when there is a velocity inversion(or inversions) within the tertiary section. Such inversions are a particular characteristic of the tertiarycarbonates. Essentially, these high incident angle rays have been refracted, and only a weak down-goingP-wave has gone through the velocity inversion boundary. It is always recommended that incident anglebe shown on PET NMO CMP gathers and often this refraction results in energy loss of the incident angleevents between 32 and 45 °.| C 3

F-K processors (e.g. F-K demultiple) that are reliant on a velocity correction which may have variableefficiency with offset, are frowned upon in true amplitude processing, but are sometimes necessary inAustralian conditions. If such a filter is used, it is recommended that the near offsets data be removedfrom any stack or sub-stack which is used in quantitative interpretation.C.3 DeconvolutionDeconvolution will invariably be required in the Australian Tertiary Environment. There are a widevariety of methods for this process, though the usual process is to provide a surface-consistent solution.Most software packages only provide a simple surface-consistent time-domain deconvolution solution.These solutions will usually require the correction of the data to minimum face, and the subsequentapplication of the T-X gapped deconvolution. This must be surface consistent because any applicationof T-X deconvolution on an un-averaged trace basis will compromise the AVO compatibility of the data.If the software and survey design allow the data to be transformed into the Tau-p domain, then this is analternative domain where a trace-by-trace deconvolution may be applied.It should also be noted that other forms of the deconvolution that purely work on a spectrum have thecapacity to create weak false AVO anomalies as well. These are a particular problem when previous notchfilters have been applied to remove any unwanted signal like 50 or 60 Hz noise and their harmonics. Anyfilter that is applied on a trace by trace basis that is based on input data has the capacity to introduce ananomaly.Experience with 3D surveys has shown that spiking deconvolution is acceptable when applied in a surfaceconsistent manner although any short gap deconvolution is frowned upon in processing for quantitativeinterpretation.C.4 Multiple removalThe methods of removal of multiples on 3-D surveys has changed rapidly over the last few years. Althoughland survey data does not have the problems that many marine surveys do, multiples are presentto some degree. Invariably, the best solution occurs when there is a significant difference between the primaryand multiple velocity fields. If there is good velocity discrimination between primary and multiple,then the data may benefit from radon demultiple. In cases of bent-line processing or 3D processing, thiscan only be effectively applied after data regularisation and subsequent migration. It is usually applied at100% (or close to) moved out gathers. For application of this process, it is recommended that the multiplebe modelled and subsequently removed to modelling the multiple and subsequently remove it either bydirect or adaptive subtraction. It should be noted that the statics solution required to model the multiplesmay not be the best static solution to stack the data with, and again, the multiples should be examinedwith and without NMO, and different statics solutions applied before progressing with production.Radon application should be tested both with and without a wrap-around automatic gain control (AGC).Wrap-around AGC is a relatively short gate AGC applied before the process which is reversed using thesame gain coefficients after the process.C 4 |

Recent developments, such as targeted multiple attenuation, where a particular reflection event is mapped,and its multiple field estimated and removed by adaptive retraction is an excellent process when the multiplesare dominated by a single high reflection event, such as a coal seam or limestone/shale boundary.Prior to such a process careful efforts must be made to ensure that the energy loss on the sections issuitable for such processes. Generally, these efforts involve the application of spherical divergences andusually, additional corrections.C.5 Amplitude correctionOnce the pre-stack data have had multiples and reverberation removed corrections for source and receivercoupling are required. This is the most important part of the true-amplitude processing for land seismicdata. Invariably, this process must be applied on a surface consistent basis. However, the selection ofthe offset ranges to be used in this calculation, and any offset correction prior to this determination willimpact this process, and determination of what is right, is nontrivial. Usually, prior to this process it isrecommended that a V 2 t and offset correction has been applied, as well as some correction for inelasticlosses. It is most important that the data going into this calculation is as noise free as possible, and oftenit is worth applying processes such as radon demultiple immediately prior to this process even if it is onlyto determine the coefficients, as the multiples should not be in the data. This process cannot be appliedafter migration.Correction of amplitude should at least include spherical divergences, which may be as simple t 2 duringprocessing, which should be corrected to a V 2 t plus an Ursin (1990) correction for offset either in themigration or immediately subsequent to it. As well there may be other losses, that require correction, inparticular ray path distortions due to shallow faulting, irregular near surface bodies, hydrocarbon-relateddiagenetic zones, or changes in reflection coefficients and associated transmission coefficients in theoverlying strata.Seismic data influenced by these processes, where the amplitude change is created by a buried attribute,are only partially corrected by a “surface-consistent amplitude correction”, and if these effects are evident,then the data’s amplitudes will have issues.There are some gain processes that can make the seismic data look good, and give a more consistentamplitude response, but their amplitude effects cannot be fully determined and evaluated in a precisequantitative interpretation project. Some of these processes may correct the data in common offset mode,others may correct the data with a positive only gain scalar determined by long-running median filters.These filters invariably will provide well tie complications, but are needed to the seismic data to beinterpretable. If these are applied, it is always suggested that the quantitative interpretation geophysicistbe shown the effect of this process if not provided a final migrated product both with and without thisprocess. If such processes allow examination and editing of gain scalars, it is it is recommended that atest be attempted using only positive gain coefficients applied, this has the capacity to correct for smalllocal transmission losses without reducing the amplitude of highly reflective geology.When such a data derived scalar is applied, the gain coefficients should be stored such that their impactcan be compared with the expected hydrocarbon amplitude boost, to ensure this process cannot create| C 5

a false amplitude comparable with the amplitude associated with hydrocarbons. Invariably these premigrationscalars are not retained in post migration archives, so the QI geophysicist should be informedof the processes applied and the location of the scalars (header byte location and archive).C.6 Attenuation correctionThe correction of inelastic losses due to attenuation (Q) should be made on data prior to quantitativeinterpretation. Q-processing requires the correction of travel time and phase to a reference frequency.Additionally, correction should be made for their preferential loss of high frequencies. Unfortunately,the higher frequencies are still usually noisy and potentially contaminated by multiples. Consequently,the amplitude correction for Q is usually gain-limited or frequency-limited or both. Usual parametersmay be limited to 10-15 dB boost.It is always recommended that the reference frequency be determined by wavelet extraction at wellswhere check shot and good-quality logs are available. Other popular applications are to apply the highestpossible reference frequency (constrained by the sampling of the dataset or vertical seismic profile).Correctly applying Q-correction is best performed within the migration. However, such applications arenot widely available, and are computationally demanding, as well as requiring a pre-stack attenuationmodel. The approximation of applying post-stack Q-amplitude and phase correction to a dataset priorto migration should be discouraged. It is safer to apply the phase correction prior to migration and theamplitude component post-migration and potentially post-stack, on each of the stack ranges.C.7 Velocity and migration velocity analysisVelocity “picking” is a vital and integral part of seismic data processing. Modern seismics rely on thestacking process to provide the best S/N improvement. As well intermediate processes like demultipleand migration require accurate velocity analysis. There are so many packages available for this processthat no situation should occur where the user is not simultaneously reviewing the stacking performance,the semblance coherence, the normal move out of the gathers, and comparing his velocity picks withwell and geological constraints. After each significant process, the velocities should be re-examined,but particularly after residual statics application. After any demultiple process, the stack solution shouldbe reviewed to see if there was any loss of primary energy or creation of false low-frequency events(which is typical of some poorly-parameterised Radon processes). The velocity picking stage itself isnot a quantitative interpretation product, and usually benefits from AGC applied to the traces going in tothe velocity-picking package.C.8 MigrationThe most common and recommended form of migration for quantitative interpretation is Kirchhoff prestacktime or depth migration for target strata with little dip. This method requires regularisation of theinput data set prior to migration. While some forms of migration that count accumulated trace densitycan be normalised in the migration process, these are invariably only close to true-amplitude migrations.The currently-available variety of beam migration routines have the most suspect amplitude responseC 6 |

and should not be used for quantitative interpretation unless the full range of dips and frequencies areproperly imaged.Kirchhoff migration suffers from aliasing when attempting to image steep depths and high frequencies.While these should have been taken into account in the survey design, there are a number of five dimensionalprocesses and binning schemes that allow extrapolation of extra traces prior to the migration. Ifattempting to perform qualitative interpretation of steeply dipping and thin beds these processes shouldbe evaluated carefully. Kirchhoff migration cannot handle turning waves, and is not suitable for complexstructures such as salt diapirs, or complex trust belts. Such structures are uncommon in Australianonshore sedimentary basins.C.9 Data calibrationOften, the best we can do is attempt to apply true relative amplitude processes in our seismic processingflow, and accept that the data will need correction to be close to true amplitude for quantitative interpretation.The processes to be applied, usually post migration, involves:• residual flattening of the data;• correction for amplitude and frequency content with offset and time/depth; and• correction of phaseThese processes require well data with check shot information and usually a long length of electric log.The long log suite is to enable comparison of the wavelet from the shallow section to the deep section tosee if residual gain corrections are required. Density and sonic logs are adequate for calibration of nearoffset data, but the shear logs are required to calibrate amplitude correction with offset.While originally frowned upon, logging while drilling is now providing high quality data that representsa good estimation of the rock properties prior to invasion by drilling fluids. In older wells (prior to 2004)conventional logging suites acquired at the completion of drilling, correction for invasion and well borecondition must be made. The early logging while drilling tools were incapable of recording all of therock properties required, or acquired them too sparsely or with too much noise to be used to this process.| C 7

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DGeophysical software used in the projectTo aid reproducability of results, this Appendix lists geophysical codes used in compiling this report.Table D.1 lists these codes, their commercial status and a website for more information. Gravity modellingcode used in Section 4.3 was converted to Mathematica from a C++ code by Gettings (2001) andis available on request. Gettings (2001) code is FOSS.Table D.1: Geophysical codes used in this project. The acronym ’FOSS’ stands for Free / Open SourceSoftware. The column ’Section’ indicates the section of this report in which a particular codewas used.Name FOSS? Section WebsiteArjuna Yes 5 http://www.amirainternational.com/WEB/site.asp?section=news&page=projectpages/p223f_softwareDelivery Yes 4.1.3 http://www.csiro.au/Organisation-Structure/Divisions/Earth-Science--Resource-Engineering/Delivery.aspxgslib Yes 1.2.1, 5 http://www.gslib.com/Leroi Yes 4.2.2.1 http://www.amirainternational.com/WEB/site.asp?section=news&page=projectpages/p223f_softwareMadagascar Yes 4.1.4 http://www.reproducibility.org/wiki/Main_PageMarco Yes 4.2.2.2 http://www.amirainternational.com/WEB/site.asp?section=news&page=projectpages/p223f_softwareutah-g3d Yes 4.3 http://thermal.gg.utah.edu/~gettings/model/OpenDTect Yes 2.1.2 http://opendtect.org/ProMax No 4.1 http://www.halliburton.com/ps/RadExPro No 4.1 http://www.radexpro.com/Tesseral No 4.1 http://www.tesseral-geo.com/| D 1

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EProject detailsThis section lists broad responsibilities for each section in this report.Table E.1: Report responsibility by section. CSIRO staff were responsible for sections not listed belowand took overall editorial responsibility.OrganisationSectionsCurtin University 3.1.2, 4.1.1.1, 4.1.4.2, 4.1.6.1, 4.1.4.3, 4.1.6.2, 4.2.2,4.2.2.3, Appendix BJoint Curtin University and CSIRO Executive summary, 5, 6, 7Makaira Pty Ltd4.1.2, Appendix C| E 1

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