Big data-driven analysis of shale gas enrichment patterns: A case study of the Wufeng–Longmaxi Formation in the Sichuan Basin and its periphery

The Wufeng–Longmaxi Formation derives its name from the Upper Ordovician Wufeng Formation and the Lower Silurian Longmaxi Formation, found in sequence in the Sichuan Basin. This formation hosts rich shale gas reservoirs, and its shale gas enrichment patterns are examined in this study using data from 1197 shale samples collected from 14 wells. Five basic and three key parameters, eight in all, are assessed for each sample. The five basic parameters include burial depth and the contents of four mineral types—quartz, clay, carbonate, and other minerals; the three key parameters, representing shale gas enrichment, are total organic carbon (TOC) cnotent, porosity, and gas content. The SHapley Additive exPlanations (SHAP) analysis originated in game theory is used here in an interpretable machine learning framework, to address issues of heterogeneous data structure, noisy relationships, and multi-objective optimization. An evaluation of the ranking, contribution values, and conditions of changes for these parameters offers new quantitative insights into shale gas enrichment patterns. A quantitative analysis of the relationship between data-sets identifies the primary factors controlling TOC , porosity, and gas content of shale gas reservoirs. The results show that TOC and porosity jointly influence gas content; mineral content has a significant impact on both, TOC and porosity; and the burial depth governs porosity which, in turn, affects the conditions under which shale gas is preserved. Input parameter thresholds are also determined and provide a basis for the establishment of quantitative criteria to evaluate shale gas enrichment. The predictive accuracy of the model used in this study is significantly improved by the step-wise addition of two input parameters, namely TOC and porosity, separately and together. Thus, the game theory method in big data-driven analysis uses a combination of TOC and porosity to evaluate the gas content with encouraging results—suggesting that these are the key parameters that indicate source rock and reservoir properties. • SHAP-based framework quantifies contributions to shale gas enrichment patterns. • TOC and porosity thresholds identified as key controls for gas content evaluation. • Game theory method enhances predictive accuracy of shale gas enrichment analysis. • Quantitative criteria for shale gas evaluation and resource development provided.