Predicting the Geothermal Gradient in Colombia: a Machine Learning Approach
This work addresses the need for cost-effective geothermal resource assessment in Colombia, but it is incremental as it applies an existing machine learning method to a new dataset in this domain.
The study tackled the problem of predicting the geothermal gradient in Colombia, where direct measurements are scarce, by using a Gradient Boosted Regression Tree algorithm on available data, achieving predictions within 12% accuracy and producing a map to guide further exploration.
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.