(299a) Probabilistic Risk and Reliability Analysis of Oil and Gas Exploration and Production in Sensitive Ecosystems

The large majority of oil and gas production facilities in Oklahoma are marginal oil wells. These wells are typically operated by small independent lease operators. The production of these marginal oil wells is on the lower edge of profitability with an average of 2 to 3 barrels per day. However, the contribution of these wells to the total onshore productions is significant (323 million barrels of oil in 2002). In order to help small, independent lease owners operate profitably while complying with strict regulations regarding accidental spills, we have developed a probabilistic risk and reliability analysis tool that provices lease managers with a quantitative risk-based decision making tool for technical and financial resource allocation.

The pristine and highly valued ecosystem of the Tallgrass Prairie Preserve, located in Osage County Oklahoma, was selected as the project study site. The risk associated with oil production at the site was estimated by combining the probability of an accidental produced fluid release with the severity of the consequences of the released fluids. Event tree analysis, reliability theory and Monte Carlo simulation are used to quantify the probability of occurrence of an event. The consequence estimate is based on the amount of material released, spreading of the released material, exposure assessment of vulnerable receptors, and cleanup costs. The financial consequences of accidental release events were estimated through (a) physical fate and transport model to calculate the extent and penetration depth of a spill; (b) the potential receptors and a risk factor were determined based on quantity of spill, distance from ground surface to groundwater table, distance to nearest potable water well from edge of impacted soil, and background groundwater quality, then; (c) the risk factor provides guidance for determining cleanup limits which coupled with the spill extent determines the cleanup costs. The results of the risk estimation is presented through ArcGIS maps of the study site.