Prediction of Geothermal Heat Flow Using XGBoost with the Applications in Coupling Coalbed Methane and Geothermal Resource
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Summary
This summary is machine-generated.An XGB model predicts geothermal heat flow (GHF) using geological data, improving coalbed methane (CBM) exploration. The model accurately maps thermal conditions, revealing higher GHF in coal-rich areas.
Area Of Science
- Geophysics
- Geochemistry
- Machine Learning
Background
- Reservoir temperature critically impacts coal, coalbed methane (CBM), and geothermal heat flow (GHF) formation and production.
- GHF is a key indicator of subsurface thermal conditions, vital for energy resource assessment.
Purpose Of The Study
- To develop and evaluate an extreme gradient boosting (XGB) model for predicting GHF in China.
- To assess the influence of geological and geophysical features on GHF and its relationship with CBM potential.
Main Methods
- An XGB model was trained using 12 geological/geophysical features on global and China-specific datasets.
- Model performance was evaluated using MAE, RMSE, and R²; feature importance was analyzed using SHAP values.
- Predicted heat flow maps were generated and compared with kriging-interpolated maps for consistency and geological coherence.
Main Results
- The China-based XGB model showed superior performance in low-heat-flow areas (R² = 0.73) compared to the global model (R² = 0.65).
- Generated heat flow maps displayed smoother spatial trends and stronger correlations with measured data.
- Significantly higher GHF was observed in medium- to high-rank coal regions, indicating enhanced CBM potential.
Conclusions
- The XGB model accurately predicts GHF, providing valuable insights for geothermal resource exploration and CBM development.
- Crustal structure and volcanic activity are dominant controls on GHF, guiding future exploration efforts.
- The model's predictive capability is particularly beneficial in data-scarce regions for energy resource assessment.
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