Analysis of Key Factors Controlling Fractured Wells Productivity in Tight Gas Condensate Reservoirs Based on Machine Learning Surrogate Models and SHAP
To address the complex factors affecting the productivity of fractured horizontal wells in tight condensate gas reservoirs, as well as the high computational costs and opaque mechanism interpretation associated with traditional numerical simulations, this study proposes and implements a quantitative evaluation method for the main productivity-controlling factors. This method integrates a machine learning surrogate model with the Shapley additive explanations (SHAP) interpretability framework. First, based on 3D geological modeling and fracture propagation simulation, a high-dimensional parameter set encompassing reservoir geology, artificial fractures, and fluid properties was constructed. Subsequently, representative samples were generated through an orthogonal experimental design. On this basis, machine learning algorithms, including Support Vector Machines (SVM), Random Forests (RF), and eXtreme Gradient Boosting (XGBoost), were utilized to construct low-cost, high-precision surrogate models targeting initial productivity and Estimated Ultimate Recovery (EUR). These surrogate models effectively substituted the computationally expensive fully coupled numerical simulations. Furthermore, SHAP values were applied to the trained surrogate models to conduct both global and local interpretability analyses. This approach not only quantifies the magnitude and direction of each input parameter’s contribution to the productivity predictions, but also reveals their non-linear mechanisms and interaction effects. The results indicate that reservoir properties and gas saturation are the fundamental factors determining the productivity of fractured horizontal wells, while fracture conductivity and fracture half-length are the key engineering factors. Furthermore, there exist significant synergistic or antagonistic effects between the geological and engineering parameters. The integrated “parametric modeling–surrogate model construction—SHAP interpretability analysis” workflow established in this study provides a highly efficient, transparent, and physically insightful novel approach for the rapid optimization of fracturing designs and the mechanistic analysis of main productivity-controlling factors in tight condensate gas reservoirs.