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.Comment: 37 pages, 15 figures, 6 table