Stanford Geothermal WorkshopFebruary 9-11, 2026

Data Science has provided geothermal reservoir managers with new avenues for making energy production from this resource more sustainable, accurate, and efficient in recent years. The focus of this Review Paper is to examine recent data science use and development, specifically using machine learning and big data analytics. New AI tools are offering incredible opportunities for field operators to incorporate this technology into their older equipment, ranging from drilling to monitoring. With continuous data generation from geothermal reservoirs, operators can utilize AI to improve decision-making, optimize resource extraction, and improve reservoir monitoring. Due to a large amount of variability in the ranges of permeability, porosity, and fluid distribution traditionally, reservoir characterization and modeling have had a great deal of uncertainty. Machine learning techniques are now being applied to analyze the geological and geophysical data to create the most representative hydrocarbon static model of each reservoir. When machine learning techniques are combined with the ability of traditional reservoir simulation to develop more accurate data driven models, then reservoir engineers will be able to work with more reliable tools for understanding reservoir behavior, history matching, and optimizing the production strategies of those reservoirs. Additionally, by combining big data and numerous datasets (production logs, well tests, or numerical simulation), physics-informed machine learning can be utilized to determine the dynamic behavior and identify the potential unseen risks of geothermal reservoir management. Additionally, the real time monitoring and predictive maintenance of geothermal reservoir operation are two other benefits of data science, where IoT enabled sensors and AI driven models allow operators to continue to monitor key parameters of the operation, where normally the environmental temperature, pressure, stress, and salinity are greater. Anomaly detection, predicting equipment failure, bottleneck detection, program optimization, reducing downtime, extending the life of reservoir infrastructure, and minimizing operational costs, etc., are some examples of how data science can aid in the traditional operation of geothermal reservoirs and improve the overall efficiency of geothermal energy production. The review addresses multiple aspects of the integration of data science and reservoir management strategies, focusing primarily on the geothermal industry. Data science, including AI, machine learning, and big data analytics, can provide the geothermal field operators and researchers with the ability to apply AI capabilities to their daily routines. Some examples of how geothermal energy can be enhanced as part of the renewable energy matrix through data-driven modeling techniques include improving decision-making, enhancing resource management, achieving sustainability goals, and identifying bottlenecks

Topic: Production Engineering