53rd U.S. Rock Mechanics/Geomechanics Symposium,
New York City, New York
2019. American Rock Mechanics Association
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47 since 2007
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ABSTRACT: Many shale oil reservoirs in the US have outstanding amounts of oil reaching several billion barrels in formations such as Bakken, Eagle Ford, Niobrara, and Marcellus. These huge resources require extensive reservoir studies and development strategies, which typically conducted through dynamic simulation models. Therefore, enhancing reservoir models is essential to mimic reservoirs performance and to predict future behavior under different constrains. In this research, Bayesian sensitivity analysis was considered to optimize the process of reservoir model history matching and to obtain the most representative reservoir model. Oil production from shale reservoirs requires the use of horizontal wells with hydraulic fractures and this technology affects reservoir performance significantly. As a result, more parameters will be enforced in the reservoir simulation models leading to exacerbate the efforts of achieving history matching. In this research, Bayesian Model selection (BMS) was applied as a stochastic solution to flag the key parameters that affect the reservoir performance and history matching dramatically. The results obtained from the best model showed that horizontal matrix permeability, fracture half-length, and rock compressibility are more sensitive than others. The novelty of Bayesian Model Selection comes from its procedure to optimize the best fitting model among 50 different models based on Bayes' theorem.
Reservoir matrix parameters along with the hydraulic fracture parameters have substantial impact on the reservoir performance as well as the process of building a representative simulation model. Defining the sensitivity of the parameters by categorizing the ones with the major effects against the others with minor effects will enhance the process reservoir modeling and make it less tedious. Consequently, sensitivity analysis is a key approach in the integrated reservoir simulation workflows for categorizing the geological controls based on their impact on model performance. Through this approach, the most represented model with the optimum history matching to be proposed out of the many generated reservoir models after analyzing the sensitivity of the 15 reservoir and hydraulic fractures parameters.
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