https://doi.org/10.1051/epjconf/202636704005
Machine Learning Based Subsurface Temperature Forecasting to Reduce Drilling Uncertainty in Geothermal Systems
1 PhD Researcher, School of Computing, Engineering and Digital Technologies, Teesside University, UK
2 Professor, School of Computing, Engineering and Digital Technologies, Teesside University, UK
* Corresponding Author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Published online: 29 April 2026
Abstract
Subsurface temperature uncertainty represents a major risk in geothermal drilling, particularly in volcanic systems where permeability, lithology, and fluid circulation create highly heterogeneous thermal regimes. This study presents a hybrid physics-informed machine learning framework for forecasting subsurface temperature using geothermal drilling and geophysical log data from Icelandic geothermal fields. A publicly available dataset from the GEOTHERMICA/RESULT project comprising 16 deep geothermal wells from the Elliðaár geothermal field was used for model development and validation. Ensemble and neural network models were optimized using Bayesian hyperparameter tuning and evaluated against conventional geothermal gradient methods. The present study represents a proof-of-concept demonstration of the proposed framework. Ongoing work is focused on expanding the dataset to 52 geothermal wells and enabling real-time deployment for geothermal drilling operations.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
