Predicting the Geothermal Gradient in Colombia: a Machine Learning Approach
<|reference_start|>Predicting the Geothermal Gradient in Colombia: a Machine Learning Approach: Accurate determination of the geothermal gradient is critical for assessing the geothermal energy potential of a given region. Of particular interest is the case of Colombia, a country with abundant geothermal resources. A history of active oil and gas exploration and production has left drilled boreholes in different geological settings, providing direct measurements of the geothermal gradient. Unfortunately, large regions of the country where geothermal resources might exist lack such measurements. Indirect geophysical measurements are costly and difficult to perform at regional scales. Computational thermal models could be constructed, but they require very detailed knowledge of the underlying geology and uniform sampling of subsurface temperatures to be well-constrained. We present an alternative approach that leverages recent advances in supervised machine learning and available direct measurements to predict the geothermal gradient in regions where only global-scale geophysical datasets and course geological knowledge are available. We find that a Gradient Boosted Regression Tree algorithm yields optimal predictions and extensively validate the trained model. We show that predictions of our model are within 12% accuracy and that independent measurements performed by other authors agree well with our model. Finnally, we present a geothermal gradient map for Colombia that highlights regions where futher exploration and data collection should be performed.<|reference_end|>
@article{mejía-fragoso2024predicting,
    title={Predicting the Geothermal Gradient in Colombia: a Machine Learning
  Approach},
    author={Juan Camilo Mej'ia-Fragoso, Manuel A. Florez, Roc'io Bernal-Olaya},
    journal={arXiv preprint arXiv:2404.05184},
    year={2024},
    doi={10.1016/j.geothermics.2024.103074},
    archivePrefix={arXiv},
    eprint={2404.05184},
    primaryClass={physics.geo-ph cs.LG}
}