Interpretable Combinatorial Machine Learning-Based Shale Fracability Evaluation Methods

Shale gas, as an important unconventional hydrocarbon resource, has attracted much attention due to its great potential and the need for energy diversification. However, shale gas reservoirs with low permeability and low porosity pose challenges for extraction, making shale fracability evaluation crucial. Conventional methods have limitations as they cannot comprehensively consider the effects of non-linear factors or quantitatively analyse the effects of factors. In this paper, an interpretable combinatorial machine learning shale fracability evaluation method is proposed, which combines XGBoost and Bayesian optimization techniques to mine the non-linear relationship between the influencing factors and fracability, and to achieve more accurate fracability evaluations with a lower error rate (maximum MAPE not more than 20%). SHAP(SHapley Additive exPlanation) value analyses were used to quantitatively assess the factor impacts, provide the characteristic importance ranking, and visualise the contribution trend through summary and dependency plots. Analyses of seven scenarios showed that ‘Vertical—Min Horizontal’ and ‘Vertical Stress’ had the greatest impact. This approach improves the accuracy and interpretability of the assessment and provides strong support for shale gas exploration and development by enhancing the understanding of the role of factors.