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addressing uncertainty in oil and natural gas industry greenhouse

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4.4 Quantify<strong>in</strong>g Measurement Uncerta<strong>in</strong>ty<br />

Ideally, one would develop emission estimates <strong>and</strong> associated <strong>uncerta<strong>in</strong>ty</strong> from measured facility data.<br />

This data could be obta<strong>in</strong>ed either from periodic sampl<strong>in</strong>g or by cont<strong>in</strong>uous monitor<strong>in</strong>g. The decision tree<br />

provided <strong>in</strong> Figure 4-4 directs the user to the appropriate equations for quantify<strong>in</strong>g measurement<br />

<strong>uncerta<strong>in</strong>ty</strong>. Follow<strong>in</strong>g the decision tree provided, we will first exam<strong>in</strong>e the equations for aggregat<strong>in</strong>g<br />

<strong>uncerta<strong>in</strong>ty</strong> for a s<strong>in</strong>gle measurement po<strong>in</strong>t.<br />

Are the data based on a s<strong>in</strong>gle<br />

po<strong>in</strong>t measurement or multiple<br />

measurements?<br />

S<strong>in</strong>gle po<strong>in</strong>t<br />

Determ<strong>in</strong>e what parameters<br />

contribute to <strong>uncerta<strong>in</strong>ty</strong>. Note that<br />

bias may be one of the parameters.<br />

Are the uncerta<strong>in</strong>ties <strong>in</strong> the<br />

measurements <strong>in</strong>dependent? (See text<br />

for guidance)<br />

Yes<br />

Apply “Square root of the sum of the<br />

squares” to aggregate <strong>uncerta<strong>in</strong>ty</strong><br />

parameters that are <strong>in</strong>dependent<br />

(Equation 4-4)<br />

No<br />

Apply Equation 4-5 to aggregate the<br />

<strong>uncerta<strong>in</strong>ty</strong> for parameters that are<br />

correlated.<br />

Although there is only a s<strong>in</strong>gle measurement value <strong>in</strong> this application, such as the cumulative flow rate<br />

through a totalizer meter, there may be more than one parameter that contributes to the <strong>uncerta<strong>in</strong>ty</strong> of the<br />

measurement. For example, Section 3.2 <strong>in</strong>dicates that the follow<strong>in</strong>g items need to be considered when<br />

estimat<strong>in</strong>g measurement <strong>uncerta<strong>in</strong>ty</strong>:<br />

• Uncerta<strong>in</strong>ty of the measurement <strong>in</strong>strument;<br />

• Additional <strong>uncerta<strong>in</strong>ty</strong> of “context specific” factors (discussed previously <strong>in</strong> Section 3.2); <strong>and</strong><br />

• Uncerta<strong>in</strong>ty of measurement corrections, e.g., pressure <strong>and</strong> temperature corrections for <strong>gas</strong> meters<br />

measurements.<br />

The aggregated <strong>uncerta<strong>in</strong>ty</strong> from these parameters is calculated by either apply<strong>in</strong>g Equation 4-4 for<br />

<strong>in</strong>dependent parameters or Equation 4-5 for correlated parameters.<br />

Example –S<strong>in</strong>gle Flow Measurement<br />

The follow<strong>in</strong>g example demonstrates the <strong>uncerta<strong>in</strong>ty</strong> calculation for estimat<strong>in</strong>g CO 2 emissions result<strong>in</strong>g<br />

from the combustion of produced <strong>natural</strong> <strong>gas</strong>. The scenario presented is based on a s<strong>in</strong>gle measurement of<br />

the flow.<br />

Pilot Version, September 2009 4-16

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