Data-driven model predictive control for closed-loop refracturing design and optimization in naturally fractured shale gas reservoir under geological uncertainty

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The reliable and predictive control for closed-loop refracturing design and optimization is of significance to improve the unconventional resource development and recovery in petroleum engineering. To reduce refracturing-decision risk under uncertainty for unconventional shale gas reservoir, we present an efficient and robust two-stage refracturing optimization workflow through hybridizing data-space inversion and non-dominated sorting genetic algorithm-II. Without performing history matching step, we adapt data-space inversion to simultaneously predict post-history pressure field and shale gas production corresponding to specific refracturing parameters given history measurement. In the proposed framework, both the enhanced net present value and enhanced cumulative gas production after refracturing stimulation are regarded as the bi-objective functions while the refracturing parameters including fracture number and fracture half-length are chosen as the decision variables. The proposed two-stage refracturing optimization strategy enables us to effectively identify the potential candidate clusters and/or wells and then search the optimal refracturing parameters efficiently and reasonably.