addressing uncertainty in oil and natural gas industry greenhouse

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when planning the compilation of an emissions inventory and could address as part of emission inventory assurance processes (API/IPIECA, OGP, 2003). Adhering to appropriate sampling, measurement, and estimation procedures – with applicable quality control and quality assurance measures – can help minimize uncertainties. Nonetheless, it is the nature of the measurement process that makes it impossible to measure a physical quantity without error. It is this collection of measurement errors and approximations that contribute largely to overall emission inventory range of uncertainty. Emission inventory development and uncertainty analysis could be viewed as components of a quality improved management process. Once estimates of uncertainty are developed, the inventory preparer could review the inventory and target the most significant sources – those exhibiting the largest range of uncertainty – for more research and refinement. Table 2-1 provides an overview of selected methods recommended by the U.S. Environmental Protection Agency (EPA) for qualitative and quantitative estimation of emissions ranges of uncertainty. The uncertainties associated with natural variability inherent to the emission process and its underlying data can be assessed by statistical analysis methods. Further discussion and specific procedures for statistical quantification of uncertainty intervals are provided in Section 4.0. Table 2-1. Overview of Methods Used to Estimate Emissions Uncertainty a METHODOLOGY Qualitative Discussion − − DESCRIPTION OF METHOD Sources of uncertainty are listed and discussed. General direction of bias and relative magnitude of imprecision are given if known. LEVEL OF EFFORT Low Subjective Data Quality Ratings − Subjective rankings based on professional judgment are assigned to each emission factor or parameter. Low Data Attribute Rating System (DARS) − Numerical values representing relative uncertainty are assigned through objective methods. Medium Expert Estimation Method − − − Experts estimate emission distribution parameters (i.e., mean, standard deviation, and distribution type). Simple analytical and graphical techniques are then used to estimate confidence limits from the assumed distributional data. In the Delphi method, expert judgment is used to estimate uncertainty directly. Medium Pilot Version, September 2009 2-2
Table 2-1. Overview of Methods Used to Estimate Emissions Uncertainty a METHODOLOGY DESCRIPTION OF METHOD LEVEL OF EFFORT Propagation of Errors Method − − Emission parameter means and standard deviations are estimated using expert judgment, measurements, or other methods. Standard statistical techniques of error propagation typically based upon Taylor’s series expansions are then used to estimate the composite uncertainty. Medium Direct Simulation Method − − − Monte Carlo, Latin hypercube, bootstrap (resampling), and other numerical methods are used to estimate directly the central value and confidence intervals of individual emission estimates. In the Monte Carlo method, expert judgment is used to estimate the values of the distribution parameters prior to performance of the Monte Carlo simulation. Other methods require no such assumptions. High Direct or Indirect Measurement (Validation) Method − − − Direct or indirect field measurements of emissions are used to compute emissions and emissions uncertainty directly. Methods include direct measurement such as stack sampling and indirect measurement such as tracer studies. These methods also provide data for validating emission estimates and emission models. High a Extracted from Table 4.1-1 of the Emissions Inventory Improvement Program (EIIP), Chapter IV: “Evaluating the Uncertainties of Emission Estimates,” U.S. EPA, Research Triangle Park, NC, July 1996 2.2 Emissions Inventory Uncertainty in the Oil & Natural Gas Industry The API Compendium (API, 2004) and its forthcoming revision (API, 2009) provide an extensive compilation and tabulation of methods that are used by companies in all the sectors of the O&G industry to calculate their GHG emissions in a consistent manner. The API Compendium provides a wide range of emission factors and other emission estimation methods that are directly applicable to all sectors of industry operations. It also exhibits the ranges of uncertainty (at the 95% confidence level) associated with each of the case study examples featured in Section 8 of the 2009 API Compendium. Related industry guidelines include those of the Interstate Natural Gas Association of America (INGAA), which provides supplemental guidance for estimating GHG emissions from key emission sources associated with natural gas storage and transmission sector of the industry (INGAA, 2005) and the AGA which has also published specific guidelines for estimating GHG emissions from gas distribution operations (AGA, 2008). O&G industry operations vary widely among its operating sectors due to the nature of the operation, its geographical locations and local practices. Operations in some of the industry sectors are highly centralized in large and complex facilities while other extend over large geographical areas, with some of Pilot Version, September 2009 2-3
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: 2.0 SOURCES OF UNCERTAINTY There ar
Page 19 and 20: the uncertainty associated with cur
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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EXHIBIT 4-5: Uncertainty Example fo
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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:
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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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