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thermal conductivity, geothermal exploration
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Thermal properties such as thermal conductivity (TC), thermal diffusivity (TD) and specific heat capacity (SHC) are essential for understanding and modelling the subsurface thermal field. In sedimentary basins, these parameters play a key role in characterizing the present-day thermal state and predicting its evolution, for example, in response to future geo-energy utilizations. Given the wide range of potential geo-energy utilizations and the frequent lack of sufficient sample material, many studies have focused on developing accurate prediction approaches. Machine learning (ML) offers promising nonlinear statistical methods to enhance the mapping of interrelations between standard geophysical well-log readings and thermal rock properties. In this study, we introduce an open-access tool for computing profiles of thermal rock properties from standard geophysical borehole logging data, building upon and extending previous petrophysical studies. The tool employs various machine-learning approaches trained on large, physically modelled synthetic data sets that account for mineralogical and porosity variability across major sedimentary rock groups (clastic rocks, carbonates and evaporates). It establishes functional relationships between thermal properties and different combinations of standard well-log data, including sonic velocity, neutron porosity, bulk density and the gamma-ray index. We trained four different models including linear regression, AdaBoost, Random Forest and XGBoost using 80 per cent of the synthetic group data for model development, including training and hyperparameter tuning through cross-validation. The remaining 20 per cent was held out as an independent test set for statistical validation, feature recognition and input variable importance analysis. A total of 15 input log combinations (including all one, two, three and four well-log configurations) were evaluated across four machine learning models (linear regression, AdaBoost, Random Forest and XGBoost), resulting in 180 trained models. The model's predictive accuracy and reliability were further evaluated against independent laboratory drill-core measurements reported in recent studies. Our results indicate that the best-performing predictive models vary depending on the available log-combinations. However, XGBoost frequently outperforms other models in sedimentary rocks. When at least two well logs are provided as input variables, the best-performing models predict thermal conductivity with an uncertainty below 10 per cent relative to borehole validation data (with laboratory-measured thermal conductivity). In most tested model cases and for most input log combinations, predictive errors for thermal conductivity range between 10 and 30 per cent at the (point measurement) sample scale (cm to half a metre). However, when averaged over geological formations or borehole intervals (tens to thousands of metres), the accuracy of thermal-conductivity predictions improves significantly, with uncertainties of the interval mean conductivity dropping below 5 per cent for large intervals. For specific heat capacity, prediction accuracy for the best-performing models at the measurement scale is typically better than 5 per cent. Thermal diffusivity reflects a larger variation, accumulating the uncertainties from conductivity and heat capacity. The presented log-based Python prediction tool provides an automated means to compute thermal parameters using the most suitable ML model for given well-log inputs, facilitating enhanced thermal characterization in sedimentary settings. This has practical relevance for geothermal or hydrocarbon exploration, or subsurface storage projects.
