Deep geothermal energy, known for its stable base load power and resilience to environmental fluctuations, is increasingly recognized as an important renewable energy source. Yet, its development is constrained by subsurface variability, high exploration costs, and operational inefficiencies. Artificial intelligence (AI) can analyze complex data, reveal patterns, and support predictive modeling to lower costs, shorten timelines, and improve efficiency. This review aims to evaluate how AI can address these barriers by systematically synthesizing its applications in deep geothermal research. A structured Web of Science search and multi-stage screening yielded 183 peer-reviewed journal papers, classified across eight research areas: reservoir characterization, exploration and resource identification, system optimization, seismic monitoring and risk assessment, drilling optimization, hybrid energy systems, environmental impact and sustainability, and techno-economic analysis. Our analysis shows that since 2020, AI applications in geothermal energy have expanded exponentially, surpassing overall AI growth rates. China and the United States dominate research output, followed by Germany, Turkey, Canada, and India. Advanced algorithms are increasingly preferred: convolutional neural networks for spatial modeling and image interpretation, recurrent neural networks for time-series forecasting, physics-informed AI, Bayesian frameworks, and autoencoders advance uncertainty quantification and data reconstruction. The novelty of this review lies in its comprehensive cross-domain synthesis of AI applications in deep geothermal energy, using a unified algorithm–input–output–performance lens. This structured mapping enables comparisons not possible in earlier overviews, reveals methodological strengths, identifies effective approaches for different geothermal tasks, and uncovers underexplored areas such as environmental assessment and techno-economic analysis.
Artificial Intelligence in Deep Geothermal Energy: Trends, Insights, and Future Perspectives
Sheini Dashtgoli, Danial
Primo
;Cherubini, Claudia;
2025-01-01
Abstract
Deep geothermal energy, known for its stable base load power and resilience to environmental fluctuations, is increasingly recognized as an important renewable energy source. Yet, its development is constrained by subsurface variability, high exploration costs, and operational inefficiencies. Artificial intelligence (AI) can analyze complex data, reveal patterns, and support predictive modeling to lower costs, shorten timelines, and improve efficiency. This review aims to evaluate how AI can address these barriers by systematically synthesizing its applications in deep geothermal research. A structured Web of Science search and multi-stage screening yielded 183 peer-reviewed journal papers, classified across eight research areas: reservoir characterization, exploration and resource identification, system optimization, seismic monitoring and risk assessment, drilling optimization, hybrid energy systems, environmental impact and sustainability, and techno-economic analysis. Our analysis shows that since 2020, AI applications in geothermal energy have expanded exponentially, surpassing overall AI growth rates. China and the United States dominate research output, followed by Germany, Turkey, Canada, and India. Advanced algorithms are increasingly preferred: convolutional neural networks for spatial modeling and image interpretation, recurrent neural networks for time-series forecasting, physics-informed AI, Bayesian frameworks, and autoencoders advance uncertainty quantification and data reconstruction. The novelty of this review lies in its comprehensive cross-domain synthesis of AI applications in deep geothermal energy, using a unified algorithm–input–output–performance lens. This structured mapping enables comparisons not possible in earlier overviews, reveals methodological strengths, identifies effective approaches for different geothermal tasks, and uncovers underexplored areas such as environmental assessment and techno-economic analysis.Pubblicazioni consigliate
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