addressing uncertainty in oil and natural gas industry greenhouse

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Additionally, Monte Carlo simulation can also be computationally intensive, with 10,000 simulations being the norm. The bigger difficulty/barrier is the need to build a simulation model in Excel for uncertainty analysis that reflects the complexity of the emissions estimation model and allows generation of the variables required for the Monte Carlo simulation software. To perform a Monte Carlo simulation, one must first determine the distribution of the data for each of the uncertain variables used in the emissions model estimate. Ideally, each of these distributions should be derived from data and knowledge of the underlying process. It is helpful in many instances to first graph the data, and using the shape of the graph to determine the underlying distributions. The parameters that define the distribution could be derived from the data. For example, a normal distribution is defined by its mean and variance. If data are limited, one may have to rely on expert judgment to determine the underlying distribution. It is then necessary to statistically test the hypothesis that the data follow a certain distribution. The test will vary based on the hypothesized distribution. For example, the Shapiro-Wilks test is often used to test if the data are normal or lognormal. Options to test for other distributions include Empirical Distribution Functions (Singh and Singh, 2006). Once distributions are determined for all of the data sources, the Monte Carlo simulation will proceed by randomly sampling each of the distributions that describe the data used for estimating emissions. As many as 10,000 replicate samples are typically taken, with the total emissions being estimated for each replicate. These repeated determinations of emission are used to generate a distribution of the total emissions with its mean being the estimate of total emissions, and its uncertainty determined by its variance. 4.6.2 Comparison of Uncertainty Propagation and Monte Carlo Section 6.3.1 of the IPCC Good Practices document compares the uncertainty propagation method and the Monte Carlo simulations (IPCC, 2006). It notes that the uncertainty propagation method’s assumption of normality leads to symmetric 95% confidence intervals whereas the Monte Carlo method can take into account the fact that emissions are bounded below by zero to fit an asymmetric (and thus narrower) confidence interval. If the data are skewed and one transforms the data (discussed earlier), one could achieve the asymmetric confidence intervals using uncertainty propagation, as well. Since the Monte Carlo simulations can assume a truncated distribution, the lower confidence limits tend to be closer to the mean than the upper confidence limits. The IPCC Good Practices document goes on to state that the two methods produce results that are fairly comparable. It recommends that countries report the results of the uncertainty propagation method and those countries with “sufficient resources and expertise” report Monte Carlo results as well. Pilot Version, September 2009 4-35
These guidelines concentrate solely on the uncertainty propagation method due to the potential to introduce further errors in assigning the probability distributions for the Monte Carlo simulations. As stated previously, applying the uncertainty propagation methods, even where the assumptions are not met, is advised in these guidelines, particularly for emission sources with a small contribution to the overall GHG inventory. As data collection methods improve for GHG inventories, the ability to quantify uncertainties will also improve. Pilot Version, September 2009 4-36
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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
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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 and 80: Table 4-13. Comparison of Annual Em
Page 81: • The evolution of activity and e
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
Page 131 and 132: 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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