addressing uncertainty in oil and natural gas industry greenhouse

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the operations not contained in traditional; “facilities”. Additionally, companies operations tend to encompass many jurisdictions, which adds to the complexity of compiling an emission inventory. Therefore, data availability may be different among industry sectors and regions due to a given sector’s operational considerations and local requirements. The uncertainty associated with CO 2 emissions from combustion would be primarily attributable to variation in the composition of combusted fuels and their respective consumption rates (or total volumes). For quantification of combustion emissions, quality data are typically available for industry facilities in all sectors, though significant effort may be required in order to collect data for smaller operating installations that are spread out in multiple locations. Moreover, since a large fraction of industry operations rely on self-generated, if is the knowledge of the carbon content of such fuels that is at the root of determining their associated CO 2 emissions. For the exploration and production sector, the composition of these self-generated fuels may vary with the nature of the producing formations, while for refining it will depend on the composition of the crude oil processed and the slate of products manufactured. On the other hand, for natural gas transmission and distribution operations, gas quality and its composition are expected to adhere to contract requirements and would vary only within a narrow specifications range. Hence, the use of average fuel compositions data has to be evaluated when compiling an emission inventory since it might result in wide uncertainty ranges for some sectors and operations, while they might be perfectly acceptable for others. A different set of parameters is important for understanding emissions associated with process vents and fugitive emissions. For many of the large process units that are found in refineries and natural gas processing plants, numerical models (equations) are available for estimating these emissions. For highpressure pipelines transmitting natural gas over long distances, the main GHG emissions are due to reciprocating engines and turbines, venting due to gas blow-down, and fugitive emissions associated with leaking piping components. For low-pressure gas distribution, most of the GHG emissions come from compressors and leaks from gas distribution mains and associated equipment. Quantifying emissions, and their associated uncertainty ranges, for venting and fugitive emissions in the exploration and production sector poses a real challenge. These emissions could be quite significant for high-pressure uncontrolled natural gas production operations or miniscule for controlled oil and associated gas production. For vented emissions, operators in the U.S., as well as other jurisdictions, typically maintain required records for reporting (and archiving) gas-venting incidents. However, U.S. reports of “lost and unaccounted for gas” from natural gas pipelines typically account from both vented and fugitive emissions. Therefore, disaggregating the data would be required in order to derive a separate average emission factor for venting incidents only. When it comes to fugitive emissions from equipment leaks, Pilot Version, September 2009 2-4
the uncertainty associated with current practices is significant. The most reliable emission factors still use mid-1990 field measurement data, and when coupled with the difficulties of obtaining reliable equipment counts for estimating such emissions, the result could exhibit large uncertainty ranges, although these emissions may be negligible within the context of the overall inventory. In summary, since the most prevalent emissions from fuel combustion is CO 2 , and from venting and fugitive emissions, CH 4 , the main contributors to the uncertainty ranges of these respective GHGs in an inventory generally are: − − For estimating CO 2 emissions – Uncertainty is primarily attributable to variation in “self generated” fuel gas composition and its associated consumption rates. Fuel gas composition could vary from location to location or from batch to batch, and therefore using average composition data may lead to a high degree of uncertainty if it is used to estimate emissions. Measurements (or knowledge) of fuel gas volumes, the gas carbon content (or calorific values), and careful review of the adequacy of the emission factors used, could help to improve data quality and minimize this uncertainty. For estimating CH 4 emissions – Uncertainty is primarily associated with estimates of vented and fugitive emissions. The records that installations are required to keep (and report) on vented or released gas might not be similar under all regimes globally. Fugitive emission estimates exhibit the highest degree of uncertainty due to the use of average emission factors per component, device or type of operation, and due to improper conversions of existing factors that are expressed in terms of volatile organic compounds (VOCs) to CH 4 . Since the CH 4 to VOC ratio varies among installations, or even within different parts of a processing plant, these average emission factors, coupled with generic conversions from VOC to CH 4 may not be the best representation of CH 4 emissions. 2.3 Sources of Measurement Uncertainty The measurement process is comprised of different steps and each can introduce uncertainty into the final results. These steps can be applied whether the measurement process involves measurements of activity data (flow volumes), fuel carbon content speciation, screening for fugitive emission leaks, or direct emissions testing. The sources of uncertainty discussed below range from methods choice to physical constraints of the measurement process itself and the processing of the data obtained. Pilot Version, September 2009 2-5
Page 1 and 2: Prepared by the LEVON Group, LLC an
Page 3 and 4: Table of Contents SECTION Page FORE
Page 5 and 6: List of Tables SECTION Page 2-1 Ove
Page 7 and 8: FOREWORD The global oil and natural
Page 9 and 10: DOCUMENT AT A GLANCE This document
Page 11 and 12: 1.0 INTRODUCTION Policymakers use e
Page 13 and 14: The Intergovernmental Panel on Clim
Page 15 and 16: 2.0 SOURCES OF UNCERTAINTY There ar
Page 17: Table 2-1. Overview of Methods Used
Page 21 and 22: d. Data processing uncertainty Typi
Page 23 and 24: d. Material Balance For some emissi
Page 25 and 26: 3.0 OVERVIEW OF MEASUREMENT PRACTIC
Page 27 and 28: 3.1 Flow Measurement Practices Cont
Page 29 and 30: All these factors should be assesse
Page 31 and 32: Table 3-1. Example of FFMS Combined
Page 33 and 34: 3.2 Uncertainties of Flow Measureme
Page 35 and 36: Table 3-3. Compilation of Specifica
Page 37 and 38: EXHIBIT 3-3: FACTORS TO CONSIDER WH
Page 39 and 40: 3.3.2 Quantifying Sampling Precisio
Page 41 and 42: Table 3-4. Summary of Selected Carb
Page 43 and 44: 3.5 Heat Content Determination The
Page 45 and 46: 3.5.2 Computational Methods Heating
Page 47 and 48: using standard methods, non-standar
Page 49 and 50: 0.6 0.4 0.2 Error 0 -0.2 -0.4 -0.6
Page 51 and 52: s ± t × (Equation 4-1) n where s
Page 53 and 54: 4.2.1 Propagation Equations There a
Page 55 and 56: For two terms that might be correla
Page 57 and 58: EXHIBIT 4-1: Uncertainty Example fo
Page 61 and 62: Further guidance for assigning unce
Page 63 and 64: 4.4 Quantifying Measurement Uncerta
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Are the data based on a single poin
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Two methods are compared. The first
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. EXHIBIT 4-6: Uncertainty Example
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EXHIBIT 4-6: Uncertainty Example fo
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Table 4-13 shows the uncertainty in
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Table 4-13. Comparison of Annual Em
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• The evolution of activity and e
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These guidelines concentrate solely
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(Reprinted from the API Compendium)
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Table 5-2. Onshore Oil Field (High
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associated with them are assigned b
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Table 5-4. Onshore Oil Field (High
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5.2.4 Propagating Assymetric Uncert
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U( rel) = U( rel) + U( rel) + U( re
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Coke burn rate approach (Equation 5
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Wt% C ∑ lbmole x lbmole C 12 lb C
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The CO 2 emissions are calculated u
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GHG Emissions, tonnes/yr 60,000 50,
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vast majority of the uncertainty fo
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6.0 REFERENCES AGA, 2008, Greenhous
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The Alberta Energy and Utilities Bo
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Appendix A GLOSSARY OF STATISTICAL
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Page 115 and 116:
Page 117 and 118:
APPENDIX B LIST OF INDUSTRY MEASURE
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Chapter 2.8B Establishment of the L
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Chapter 11.5.3 Chapter 12.1.1 Chapt
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Chapter 19.4 Chapter 20.1 RP 85 RP
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ISO 8222:2002 ISO 8697:1999 ISO 902
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ISO 7194:2008 Measurement of fluid
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ASTM D4292 ASTM D4892 ASTM D5002 AS
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APPENDIX C OPERATING CONDITIONS, IN
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Appendix C OPERATING CONDITIONS, IN
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Appendix D SELECT MEASUREMENT METHO
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c. ASTM UOP539-97, Refinery Gas Ana
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interference between known componen
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maintained over the room temperatur
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APPENDIX E UNITS CONVERSION
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− Gasoline density (average) = 0.
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Appendix F UNCERTAINTY ESTIMATION D
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The uncertainty for the heaters/reb
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∑ 2 U ( abs) = U( abs) ∑(Wt%C)
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Combining the fuel usage for the tw
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Uncertainty Estimate of CO 2 e Emis
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U ( abs) = ( U ( abs) + U ( abs) +
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E E CO2e 219 tonne CO ⎛ 2 0.0108
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E CO 2 6 500× 10 scf gas lbmole ga
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• Fuel Economy (miles per gallon)
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Upper: U ( abs) = (U( abs) +U( abs)
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default CH 4 content is 5.53% from
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Uncertainty Estimate of CO 2 e Emis
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( ) Urel ( ) = Urel ( ) = Urel ( )
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Uncertainty Estimate for CH 4 and C
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specific mole % for CH 4 and CO 2 i
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E CO2 0.07239 tonne CH 4 tonne mole
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U ( rel) = U( rel) +U( rel) +U( rel
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Table F-14. Fugitive Emission and U
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U ( abs) = (U( abs) +U( abs) +U( ab
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Table F-15. Onshore Oil Field (High
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