addressing uncertainty in oil and natural gas industry greenhouse

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4.5.1 Rounding-off of Statistical Estimate Inappropriate rounding of the data can lead to errors in the final estimate. Using computer software, such as spreadsheets, helps the user avoid rounding during intermediate steps. The estimate of emissions should be rounded to smallest unit of measure (API, 13.1.8.3, 1985). The uncertainty should be rounded to the same number of digits as the estimate. 4.6 Assessing Data Correlations As shown in Equations 4-5 and 4-7, the uncertainty propagation equation can be extended to account for uncertainty terms that are correlated or not independent. A simple way to assess if the uncertainties are correlated is to examine a graph of the uncertainties. If there is no pattern, the uncertainties are most likely independent. If there is a pattern to the uncertainties, they are not independent. The correlation between the uncertainties of two measured parameters can be calculated by applying the following equation: r UX , UY = ∑ ( UX − U ) ( ) i X × UY −U i Y ( n− 1) × s( U ) × s( U ) X Y where r UxUy = the correlation coefficient of the uncertainties for X and Y; n = the sample size; Ux i = the uncertainties associated with sample points from source X; U = the mean of the uncertainties from source X; X U = the mean of the uncertainties from source y; Y Uy i = the uncertainties associated with sample points from source Y; s(U X ) = the standard deviation for the uncertainties of source X; and s(U Y ) = the standard deviation for the uncertainties of source Y. (Equation 4-9) Before combining the data it is important to eliminate the correlation of uncertainties, if possible. ISO 5168:2005(E) Annex F discusses methods for making measurement uncertainties independent, such as calibrating instruments against different references and “redefining mathematical relationships to eliminate correlations” (ISO, 2005). Section A1.4.5 of the IPCC Good Practices document lists four sources for correlation: • Use of common activity data for several emissions estimates; • Mutual constraints on a group of emission estimates (such as a specified total fuel usage which provides input to a number of processes); Pilot Version, September 2009 4-33
• The evolution of activity and emission factors associated with new processes, technology etc., which decouples the uncertainty from one time period to the next; and • External drivers that affect a suite of emissions (economic, climatic, resource based) (IPCC, 2001). To eliminate uncertainty correlations due to common activity data, IPCC suggests summing the emission factor estimates and then multiplying the activity factor by the sum to obtain total emissions. For mutual constraint, the IPCC Good Practices document suggests “to leave one of the proportions unspecified, and to determine it by the difference between the other proportions and the total fraction.” In addition, IPCC’s Good Practices document recommends that if there are more than two correlated variables, a Monte Carlo simulation should be applied instead of uncertainty propagation. 4.6.1 Monte Carlo Simulation Monte Carlo simulation is a more complex, model-based method for iteratively evaluating uncertainty associated with individual parameters. It may be the preferred approach where more complex equations are assessed. It is one of many methods for analyzing uncertainty propagation where the goal is to determine how random variation, lack of knowledge, or error affects the sensitivity, performance, or reliability of the resulting emission inventory. Monte Carlo simulation is a “repeated sampling and calculation method” because the inputs are randomly generated from probability distributions of the respective variables to simulate the process of sampling from an actual population. Inputs are defined as probabilistic parameters rather than simple estimates. The uncertainty model relies on repeated random sampling of all inputs and simultaneous recalculation of emissions (outputs) to measure variation over the course of numerous model iterations. The data generated from the Monte Carlo simulation can be represented as probability distributions (or histograms) or converted to error bars and confidence intervals. Monte Carlo simulation has an advantage over uncertainty propagation in that one can specify multivariate distributions to account for correlations between different sources of uncertainty. The IPCC Good Practices document recommends choosing one of the following distributions: normal, lognormal, uniform, or triangular. 1 The major difficulty in using the Monte Carlo simulation is the need to determine the distribution of the data. If there are not enough data to assume normality by the Central Limit Theorem (more than 30 data points), there are most likely not enough data to determine the underlying distribution of the data. Consequently, such analyses are often forced to rely on subject matter expert opinion to determine distributions rather than on observed data. 1 The IPCC Good Practice document discusses how to perform Monte Carlo simulation in Section 6.2 (IPCC, 2006). It discusses choice of distribution in Section A1.2.5. Pilot Version, September 2009 4-34
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Prepared by the LEVON Group, LLC an
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Table of Contents SECTION Page FORE
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List of Tables SECTION Page 2-1 Ove
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FOREWORD The global oil and natural
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DOCUMENT AT A GLANCE This document
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1.0 INTRODUCTION Policymakers use e
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The Intergovernmental Panel on Clim
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2.0 SOURCES OF UNCERTAINTY There ar
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Table 2-1. Overview of Methods Used
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the uncertainty associated with cur
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d. Data processing uncertainty Typi
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d. Material Balance For some emissi
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3.0 OVERVIEW OF MEASUREMENT PRACTIC
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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
Page 69 and 70: Are the data based on a single poin
Page 71 and 72: Two methods are compared. The first
Page 73 and 74: . EXHIBIT 4-6: Uncertainty Example
Page 77 and 78: Table 4-13 shows the uncertainty in
Page 79: Table 4-13. Comparison of Annual Em
Page 83 and 84: These guidelines concentrate solely
Page 85 and 86: (Reprinted from the API Compendium)
Page 87 and 88: Table 5-2. Onshore Oil Field (High
Page 89 and 90: associated with them are assigned b
Page 91 and 92: Table 5-4. Onshore Oil Field (High
Page 93 and 94: 5.2.4 Propagating Assymetric Uncert
Page 95 and 96: U( rel) = U( rel) + U( rel) + U( re
Page 97 and 98: Coke burn rate approach (Equation 5
Page 99 and 100: Wt% C ∑ lbmole x lbmole C 12 lb C
Page 101 and 102: The CO 2 emissions are calculated u
Page 103 and 104: GHG Emissions, tonnes/yr 60,000 50,
Page 105 and 106: vast majority of the uncertainty fo
Page 107 and 108: 6.0 REFERENCES AGA, 2008, Greenhous
Page 109 and 110: The Alberta Energy and Utilities Bo
Page 111 and 112: Appendix A GLOSSARY OF STATISTICAL
Page 117 and 118: APPENDIX B LIST OF INDUSTRY MEASURE
Page 119 and 120: Chapter 2.8B Establishment of the L
Page 121 and 122: Chapter 11.5.3 Chapter 12.1.1 Chapt
Page 123 and 124: Chapter 19.4 Chapter 20.1 RP 85 RP
Page 125 and 126: ISO 8222:2002 ISO 8697:1999 ISO 902
Page 127 and 128: ISO 7194:2008 Measurement of fluid
Page 129 and 130: 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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