Evaluation and Selection of Oil Shale Extraction Methods Using Multi-Attribute Utility Theory Open Access
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In the United States, on-site (in-situ) oil shale extraction methods are underutilized, leading to a potential loss of $20 billion per year in profit. On-site oil shale extraction methods are not considered when discussing large-scale oil shale extraction operations. Off-site (ex-situ) extraction remains the only commercialized method, but on-site extraction is showing considerable promise. The on-site methods allow for the exploitation of deeper oil shale prospects, which have the potential of producing up to 1.32 trillion barrels of oil. Oil shale is an unconventional hydrocarbon resource that must be considered part of the overall energy solution. There are two general methods for extracting oil shale resources: off-site extraction and on-site extraction. The off-site method consists of surface mining and above ground heating. The on-site methods consist of well drilling, well completion, and underground heating. Due to their technical differences, a comparison of the methods is difficult. This praxis seeks to inform the selection of oil shale extraction methods using a multi-attribute utility function as part of a decision tool. The tool consists of three components: the oil production model, the cost model, and the decision model. The practical application of the tool is to bring on-site extraction methods into the conversation. The Piceance Basin, located in western Colorado, is used to show the application of the tool in the field. With the ability to evaluate different extraction methods and process-heating options using the decision tool, the exploitation of more oil shale is possible. Multi-criteria decision-making methods have seen increased use in process industries. Decision making in mining and offshore oil and gas exploration provides useful insights into how decisions are made and how decision analysis could be used for oil shale projects. When evaluating distinct oil shale extraction alternatives, a decision tool, built around multi-attribute utility theory, simulation, and regression analysis provides the best vehicle for systematic decision making.