addressing uncertainty in oil and natural gas industry greenhouse

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a. Measurement methods Uncertainty Uncertainty due to measurement methods is defined as those additional uncertainty sources that originate from the techniques or methods inherent in the measurement process, especially if the methods are not “fit for use.” These uncertainty sources that might impact the measurement system can significantly affect the final results. Some common sources of measurement system uncertainties are listed in Exhibit 2-1. EXHIBIT 2-1: COMMON SOURCES OF MEASUREMENT SYSTEM UNCERTAINTY • Assumptions or constants used in the calculations • Improper placement of monitoring device or extraction of unrepresentative samples • Spatial or profile uncertainty in the conversion from discrete measurement of a representative emission factor for similar processes in other locations • Environmental effects on measurement instruments, such as heat transfer effects on a temperature probe, or pressure considerations for flow measurements − • Drift of an instrument between successive calibrations • Electrical interference with electronic components • Variation between the calibration and usage conditions b. Calibration uncertainty Manufacturers and companies typically calibrate measurement instruments before they are used in the field or in a plant. However, instrument calibration needs to be checked periodically to detect instrument drift and thus reduce measurement uncertainty. The calibration process could achieve that goal if the process is traceable to a known reference standard and allowance is made for adjustments of the measurement instrument if a bias is detected during the calibration process. . c. Data acquisition uncertainty Uncertainties in data acquisition systems depend on system design. For manual data collection – and more specifically data entry – human error can be a factor. If hard copy data are used, misplaced records could contribute to uncertainty. For instrumental data acquisition, uncertainty can arise from the signal conditioning, and the sensors or recording devices used. Quality control techniques and robust data management practices can minimize the effects of many of these uncertainty sources. For example, comparing known input values with their measured or computed results can provide an estimate of the data acquisition uncertainty. However, it is not always possible to do this in practice. In these cases, it is necessary to evaluate each potential error and aggregate these errors to assess the overall uncertainty. Pilot Version, September 2009 2-6
d. Data processing uncertainty Typical uncertainty sources in this category may stem from data scatter during the calibration process. While the equation obtained from a regression analysis of the calibration data represents the best fit, the data scatter about the curve indicates that, with more data, a slightly different equation would be obtained in the same way, because the mean of a set of data will change as more values are obtained. Thus, each coefficient in a regression equation will have an uncertainty associated with it, just as the mean of a set of values does. 2.4 Emission Estimation Approaches Emissions information is typically obtained either through direct on-site measurement of emissions or by using an emission estimation equation or model. Emission estimates are used for facility permitting, development of control strategies, compliance review, and demonstration of attainment of environmental goals. The four basic approaches for estimating emissions are: a. Emissions Factors Emissions factors are at the core of calculating GHG emissions when compiling O&G industry inventories. An emissions factor relates the rate of emission of a specific compound with an activity rate associated with its release. The general equation for using emission factors for calculating emissions is: Emissions = Activity Rate x Emissions Factor (Equation 2-1) In practice, for estimating GHG emissions from the O&G industry operations this means: − For CO 2 Emissions = Volumetric Gas Flow x Carbon Content (Equation 2-2) – or – Emissions = Fuel Energy Consumption x Carbon per Heating Value Unit (Equation 2-3) − For CH 4 Emissions = Component or Event Count x Emission Factor (Equation 2-4) When calculating emissions and their associated uncertainties, it is important to note that the overall uncertainty is based both on activity data (process flow, throughput, usage, or equipment count) and on the emission factors used. Each contributor to uncertainty should be assessed independently and then aggregated in the final analysis, as shown in Figure 4-3 in Section 4, and in the associated examples provided. Pilot Version, September 2009 2-7
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 and 16: 2.0 SOURCES OF UNCERTAINTY There ar
Page 17 and 18: Table 2-1. Overview of Methods Used
Page 19: the uncertainty associated with cur
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
Page 69 and 70: 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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