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A comprehensive and precise analysis of shale gas production performance iscrucial for evaluating resource potential, designing field development plan,and making investment decisions. However, quantitative analysis can bechallenging because production performance is dominated by a complexinteraction among a series of geological and engineering factors. In thisstudy, we propose a hybrid data-driven procedure for analyzing shale gasproduction performance, which consists of a complete workflow for dominantfactor analysis, production forecast, and development optimization. Morespecifically, game theory and machine learning models are coupled to determinethe dominating geological and engineering factors. The Shapley value withdefinite physical meanings is employed to quantitatively measure the effects ofindividual factors. A multi-model-fused stacked model is trained for productionforecast, on the basis of which derivative-free optimization algorithms areintroduced to optimize the development plan. The complete workflow is validatedwith actual production data collected from the Fuling shale gas field, SichuanBasin, China. The validation results show that the proposed procedure can drawrigorous conclusions with quantified evidence and thereby provide specific andreliable suggestions for development plan optimization. Comparing withtraditional and experience-based approaches, the hybrid data-driven procedureis advanced in terms of both efficiency and accuracy.

Hybrid Data-driven Framework for Shale Gas Production Performance Analysis via Game Theory, Machine Learning and Optimization Approaches