Leveraging Machine Learning for Subsurface Geothermal Energy Development

Geothermal energy, which derives heat from the Earth's core, presents a promising renewable resource for meeting sustainable global energy needs. Nevertheless, challenges including high initial costs, technical risks, and complex underground conditions have limited its widespread adoption. Recent advancements in Machine Learning (ML), a subset of Artificial Intelligence (AI), offer innovative solutions to these challenges. This paper presents a comprehensive review of the application of ML techniques in geothermal energy development, focusing on exploration, drilling, reservoir characterization and engineering, as well as production/injection engineering. Various ML algorithms including neural networks, clustering methods, and decision trees, have been employed to analyze complex geological and operational data. These applications have led to improved identification of geothermal resources, optimized drilling operations, enhanced reservoir management, and increased production efficiency. While ML integration offers significant advantages, limitations like data quality issues and computational demands persist. This paper highlights the need for interdisciplinary collaboration, data sharing, and increased investment in research and development to overcome these challenges. The ongoing advancement of AI technologies is anticipated to drive innovation in geothermal exploration and development, enhancing the efficiency, reliability, and economic viability of geothermal energy as a cornerstone of sustainable energy systems.