addressing uncertainty in oil and natural gas industry greenhouse

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