In this paper, a pseudo-component black oil reservoir model was constructed to simulate CO2 flooding in shale oil reservoirs. The CO2 flooding was conducted using two different approaches: cyclic injection with 10 wells (5 producers and 5 injectors) and Huff-n-Puff with only 5 wells for injection and production with same number and length of fractures. The cyclic injection obtained 38% more cumulative oil during the thirty-year prediction period than the Huff-n-Puff process. Next, Design of Experiments and Proxy modeling was adopted for the optimization of hydraulic fracturing design through the cyclic CO2 flooding. Four factors were considered in the optimization: fracture half-length, primary width, permeability, and effective width. Many experiments were designed by mixing the levels of these four factors using Latin Hypercube Sampling. The optimization approach significantly improved the cyclic injection cumulative oil production by 12%. Two proxy models were constructed to obtain a simplified alternative metamodel to the large reservoir simulator. The RBF Neural Network created a much more accurate matching of cumulative oil production calculated from the simulator and predicted from the proxy model than the polynomial regression. The most influential fracturing factors were also identified for their effects on the shale oil reservoir performance.

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