A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential, designing field development plan, and making investment decisions. However, quantitative analysis can be challenging because production performance is dominated by a complex interaction among a series of geological and engineering factors. In this study, we propose a hybrid data-driven procedure for analyzing shale gas production performance, which consists of a complete workflow for dominant factor analysis, production forecast, and development optimization. More specifically, game theory and machine learning models are coupled to determine the dominating geological and engineering factors. The Shapley value with definite physical meanings is employed to quantitatively measure the effects of individual factors. A multi-model-fused stacked model is trained for production forecast, on the basis of which derivative-free optimization algorithms are introduced 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.
翻译:对页岩气生产绩效进行全面和精确的分析,对于评估资源潜力、设计实地发展计划和作出投资决定至关重要,然而,定量分析可能具有挑战性,因为生产绩效主要取决于一系列地质和工程因素之间的复杂互动。在本研究中,我们建议采用由数据驱动的混合程序来分析页岩气生产绩效,其中包括主导要素分析、生产预测和发展优化的完整工作流程。更具体地说,游戏理论和机器学习模型相结合,以确定占支配地位的地质和工程因素。具有明确物理含义的模糊值被用于量化衡量个别因素的影响。使用多模型的堆叠模型用于生产预测,在此基础上采用无衍生物优化算法来优化发展计划。完整的工作流程由从中国四川盆地富林岩气场收集的实际生产数据验证。验证结果表明,拟议的程序可以得出严格的结论,并附有量化的证据,从而为发展规划的优化提供具体和可靠的建议。结合传统和经验方法,混合数据驱动程序在效率和效率两方面都得到推进。