Uncertainty of oil field GHG emissions resulting from information gaps: a Monte Carlo approach.

Regulations on greenhouse gas (GHG) emissions from liquid fuel production generally work with incomplete data about oil production operations. We study the effect of incomplete information on estimates of GHG emissions from oil production operations. Data from California oil fields are used to generate probability distributions for eight oil field parameters previously found to affect GHG emissions. We use Monte Carlo (MC) analysis on three example oil fields to assess the change in uncertainty associated with learning of information. Single factor uncertainties are most sensitive to ignorance about water-oil ratio (WOR) and steam-oil ratio (SOR), resulting in distributions with coefficients of variation (CV) of 0.1-0.9 and 0.5, respectively. Using a combinatorial uncertainty analysis, we find that only a small number of variables need to be learned to greatly improve on the accuracy of MC mean. At most, three pieces of data are required to reduce bias in MC mean to less than 5% (absolute). However, the parameters of key importance in reducing uncertainty depend on oil field characteristics and on the metric of uncertainty applied. Bias in MC mean can remain after multiple pieces of information are learned, if key pieces of information are left unknown.