AIIN GEOTHERMAL RESOURCE EXPLORATION – A CRITICAL REVIEW

Geothermal exploration carries its heaviest risk exactly where knowledge is thinnest: before drilling confirms the resource. This Geologic®-led 2024 review is the first to examine artificial intelligence specifically through the lens of geothermal resource exploration — the processes that discover potential before a commercial resource is confirmed. It organises a decade of machine learning research into six application domains: play fairway analysis, integrated subsurface datasets, geochemical data, geophysical data, thermal data and other data sources, and critically evaluates the algorithms, results and limitations of each.

The verdict is clear and quantified. Neural networks are the most widely used technique across every exploration group; geophysical data analysis is the most developed application; and the best reviewed deep learning classifier located geothermal sites with 92 to 95% accuracy on independent test data. AI-enhanced geothermometers, temperature-from-resistivity models and fault-detection networks now outperform their classical counterparts in study after study. What holds the field back is not algorithms but data: access, quality and the shortage of tailored data science skills in the geothermal workforce. The review closes with a practical agenda of data engineering, shared repositories and transfer learning to carry AI from research into exploration programs.