Hybrid data-driven framework for shale gas production performance analysis via game theory, machine learning, and optimization approaches

Abstract

A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential, designing a field development plan, and making investment decisions. However, quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors. In fact, each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models. Inspired by the idea, we propose a hybrid data-driven analysis framework in this study, where the contributions of dominant factors are quantitatively evaluated, the productions are precisely forecasted, and the development optimization suggestions are comprehensively generated. More specifically, game theory and machine learning models are coupled to determine the dominating geological and engineering factors. The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors. A multi-model-fused stacked model is trained for production forecast, which provides the basis for derivative-free optimization algorithms to optimize the development plan. The complete workflow is validated with actual production data collected from the Fuling shale gas field, Sichuan Basin, China. The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization. Comparing with traditional and experience-based approaches, the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.