Proxy model-driven optimization of CO2 operating condition and hydraulic fracturing design for maximizing EGR-CCS performance in the Duvernay shale formation, Canada

초록

Shale gas is a prominent unconventional resource because of the advances in horizontal drilling and hydraulic fracturing, especially in North America. Shale gas reservoirs have also been considered for carbon capture and storage (CCS) to help mitigate CO2 emissions and allow additional gas production. Enhanced gas recovery with CCS (EGR-CCS) injects CO2 to displace methane (CH4), leveraging the higher adsorption capacity of CO2 in shale. On the other hand, cumulative CH4 production and CO2 stored amount depend heavily on the timing of the injection, while economic factors such as natural gas prices and CO2 tax credits also play a role-often overlooked in previous studies. This study developed a machine learning-based proxy model to predict the net present value (NPV) of an EGR-CCS project by integrating the CO2 operating conditions, hydraulic fracturing designs, and economic factors. Based on data from the Duvernay shale reservoir, 200 scenarios were simulated to generate a training dataset. Five regression algorithms were tested. Of these five, extreme gradient boosting (XGB) yielded the highest accuracy, predicting CO2 stored amount, cumulative CH4 production, and NPV with R-2 > 0.9. A complete factorial design was then implemented to optimize the EGR-CCS process under varying economic conditions. This comprehensive framework aids decision-making to maximize the economics of EGR-CCS, highlighting the potential of the Duvernay shale formation as a geological carbon storage target.

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