addressing uncertainty in oil and natural gas industry greenhouse

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number of observations, the standard deviation of the mean decreases as the number of observations increases. Estimating uncertainties in emission inventories is based on the characteristics of the variable(s) of interest (input quantities) as estimated from the corresponding data set. The statistical computations could entail the determination of: − − − − − The arithmetic mean (mean) of the data set; The standard deviation of the data set (the standard error, the square root of the variance); The standard deviation of the mean (the standard error of the mean); The probability distribution of the data; and Covariances of the input quantity with other input quantities used in the inventory calculations. The limits of the confidence interval associated with GHG emissions from a source are directly dependent on the probability distribution, or the probability function, used to represent that data set. For some probability distributions, there are analytical relationships that relate the standard deviation to the required confidence intervals. For example, when a normal distribution is assumed for the variable under consideration, the confidence limits would be symmetric about the mean, and for a 95% confidence interval the confidence limits are approximately 2 standard deviations above and below the mean. Hence, the quantification of uncertainty intervals for calculated GHG emissions will depend both on the accuracy and representativeness of measurement data used and the assumed distributions of other key parameters used in the computations. The uncertainties associated with both emission factors and activity data could be best described by probability density functions that are constructed from available data. The applicable shapes of these probability density functions could be either determined empirically or by expert judgment, following procedures described in many guideline documents and standards (ISO, 2005; IPCC, 2000). This section has introduced the basic concepts that are germane to estimating the uncertainty range of GHG emissions. The applicable statistical calculation procedures are presented in greater detail in Section 4.0. Pilot Version, September 2009 1-4
2.0 SOURCES OF UNCERTAINTY There are a myriad of sources that contribute to the uncertainty of an emission inventory. Whether at the national, entity, or facility level, the ability to quantify emissions and understand their associated uncertainty hinges on two main factors: Section Focus This section discusses the major sources affecting the uncertainty of GHG inventories. It moves from general concepts to issues that are germane to GHG inventories in the O&G industry. It also describes factors that could − Readily available data for emission quantification; and introduce errors into the emission measurements process and contribute to the − range of uncertainties of estimated emissions. Knowledge of input parameters for statistical The subsections address: calculation of uncertainty. • Overview of emissions inventory uncertainty; The overall range of uncertainty associated with an entity GHG inventory usually is determined primarily by the • Emission inventories uncertainties in the O&G industry; uncertainty associated with the largest (“key”) sources of • Sources of measurements uncertainty; emissions. Although very large confidence intervals may be • Emission estimation approaches; and associated with the data used to characterize some small • Inventory steps and data aggregation. sources, the overall impact on the range of uncertainty at the entity, or installation level, may often be very small. In turn, the confidence interval associated with each individual source depends on the availability of sufficient data to estimate emissions, or on the quality of the data in order to properly account for emission variability. 2.1 Overview of Emissions Inventory Uncertainty Uncertainties in inventories are the result of three error categories: − − − Spurious errors, which may be due to incomplete, unclear, or faulty definitions of emission sources that result from human error or machine malfunction; Systematic errors, which may be due to the methods (or models) used to quantify emissions for the process under consideration; and Random errors, which may be due to natural variability of the process that produces the emissions. When assessing the process or quantity under consideration, uncertainties might be associated with one or more factors such as: sampling, measuring, incomplete reference data, or inconclusive expert judgment. Uncertainties due to models or equations are related to the proper application of estimation methodologies to the respective source categories. These errors are typically eliminated as far as possible in advance, Pilot Version, September 2009 2-1
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: The Intergovernmental Panel on Clim
Page 17 and 18: Table 2-1. Overview of Methods Used
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 59 and 60: EXHIBIT 4-2: 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 4-4: 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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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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