addressing uncertainty in oil and natural gas industry greenhouse

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where r = the correlation coefficient between U X , U Y , (discussed further in Sections 4.2.2 and 4.6). However, the IPCC Good Practices guidance states, “Once the summation exceeds two terms and the covariance occurs, the use of the Monte Carlo approach is preferable where data resources are available” (IPCC, 2001). b. Uncertainty Propagation for a Product (or Quotient) The equation for propagating uncertainties from the product or quotient of two or more measured and independent quantities is similar to Equation 4-4. However, in this case the relative uncertainties are used, as shown in Equation 4-6. When multiplied by 100, the resulting combined uncertainty (U(Rel) XxYxN ) is expressed as a percentage. 2 2 ⎛UX ⎞ ⎛UY ⎞ ⎛U N ⎞ Urel ( ) X× Y× ... × N= Urel ( ) X÷ Y÷ ... ÷ N= ⎜ ⎟ + ⎜ ⎟ + ... + ⎜ ⎟ ⎝ X ⎠ ⎝ Y ⎠ ⎝ N ⎠ 2 (Equation 4-6) Equation 4-7 is used to estimate the uncertainty of a product or quotient of two parameters (X and Y) where the uncertainties are correlated and positive values. Here also, relative uncertainty values are used in the equation and the resulting combined uncertainty is on a relative basis. 2 2 ⎛UX ⎞ ⎛UY ⎞ ⎛UX UY ⎞ Urel ( ) Correlated X × Y = ⎜ ⎟ + ⎜ ⎟ + 2r⎜ × ⎟ ⎝ X ⎠ ⎝ Y ⎠ ⎝ X Y ⎠ (Equation 4-7) c. Combining Uncertainties It may be necessary to combine multiple uncertainty parameters associated with a single measured value, such as combining uncertainties for precision and bias. For uncertainty parameters that are independent, the combined uncertainty is calculated using the absolute uncertainties as shown in Equation 4-4. Similarly, for uncertainty parameters that are related to each other, Equation 4-5 applies. 4.2.2 Correlation Coefficient The correlation coefficient, r, used in Equations 4-5 and 4-7, is a number between -1 and 1 that measures the linear relationship between the errors or uncertainties of two measured parameters. The value of r is zero when the parameters are independent. As stated previously, once the uncertainty propagation exceeds two terms and covariance occurs, the use of the Monte Carlo approach (described further in Section 4.6.1) is preferable (IPCC, 2001). Additional details on calculating the correlation coefficient are provided in Section 4.6. A simplified explanation follows. Pilot Version, September 2009 4-7
For two terms that might be correlated, the errors or uncertainties are plotted against each other. For the purpose of this discussion, U x represents the uncertainties of one variable plotted along the x-axis, and U y represents the uncertainties of the second variable plotted on the y-axis. The correlation coefficient, r, is determined by a linear regression of the U x and U y values. If one suspects that the uncertainty parameters are correlated, but data are not available to plot or calculate the correlation coefficient, the following rule-of-thumb values could be applied using expert judgment (Franzblau, 1958): o o o o o r = 0: no correlation, the data are independent r = ±0.2: weak correlation r = ±0.5: medium correlation r = ±0.8: strong correlation r = ±1: perfect correlation, the data fall on a straight line. 4.3 Quantifying Emission Estimation Uncertainty For the purpose of these guidelines, the general steps for quantifying uncertainty are: 1. Determine the uncertainty for emission factor data; 2. Determine the uncertainty for measured data; and 3. Aggregate uncertainties. The following provides two simple examples of uncertainty calculations for emissions estimation methods common in the oil and gas industry. 4.3.1 Simple Emission Estimation: EF × AF In constructing a facility or entity-wide GHG inventory, many of the emission estimates are based on a simple multiplication of the emission factor (EF) by a measure of the activity (AF, or activity factor). The following example demonstrates how uncertainty is determined for this type of emission estimate. Pilot Version, September 2009 4-8
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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 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: 4.2.1 Propagation Equations There a
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 and 82: • The evolution of activity and e
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,
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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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