Prediction of Geothermal Heat Flow Using XGBoost with the Applications in Coupling Coalbed Methane and Geothermal Resource

Reservoir temperature is a critical factor influencing the formation and production of coal, coalbed methane (CBM), and geothermal heat flow (GHF), with GHF serving as a key indicator of subsurface thermal conditions. In this study, an extreme gradient boosting (XGB) model is developed to predict GHF in China using 12 geological and geophysical features. The model is trained on both global and China-specific data sets and evaluated using MAE, RMSE, and <i>R</i> ² metrics. Feature importance is assessed via weight, gain, coverage, and SHAP values. Results show that the China-based model performs better in low-heat-flow areas (<i>R</i> ² = 0.73), while the global model is more accurate in high-heat-flow regions (<i>R</i> ² = 0.65). Based on predictions, heat flow maps were generated for China and Shanxi Province and compared to kriging-interpolated maps. The model-generated maps exhibit smoother spatial trends and stronger correlations with measured data, demonstrating improved consistency and geological coherence. Statistical analysis reveals that GHF in medium- to high-rank coal regions, favorable for CBM generation, is significantly higher than in low-rank regions. In Shanxi, coal-bearing areas also show elevated GHF compared to noncoal regions, reinforcing the link between thermal conditions and CBM potential. Feature importance analysis highlights crustal structure and volcanic activity (e.g., distance to volcanoes, distance to mid-ocean ridges, and Moho depth) as dominant GHF controls. The XGB model demonstrates robust predictive capability, offering valuable guidance for geothermal resource exploration and CBM development, particularly in data-scarce regions.