AI and machine learning is rapidly becoming more advanced and useful to the geothermal industry. These recent advancements create new opportunities for innovation and system optimizations to increase the probability of exploration and development success while helping to drive down the overall cost of geothermal energy. This session will highlight recent improvements and applications of AI and machine learning in the geothermal industry.

• 7:30 AM - 7:50 AM | Open-Source Value of Information App: Insight from Three Geothermal Case Studies | Presenting Author: Whitney Trainor-Guitton
• 7:50 AM - 8:10 AM | Machine Learning to Delineate Concealed Hydrothermal Resources in the Rio Grande Rift Zone of Southwestern United States | Presenting Author: Shuvajit Bhattacharya
• 8:10 AM - 8:30 AM | Machine Learning for Decline Curve Analysis: Beyond Arps Model | Presenting Author: Emmanuel Gyimah
• 8:30 AM - 8:50 AM | Preventing Overfitting When Using Tree-Based Methods for Mapping Hydrothermal Favorability | Presenting Author: Eric Burns
• 8:50 AM - 9:10 AM | Machine Learning-Based Prediction of Fracture Characteristics from Drilling Data in Geothermal Wells | Presenting Author: Khomchan Promneewat
• 9:10 AM - 9:30 AM | Advancing Geothermal Drilling Efficiency Through Multi-Prediction and Hybrid Machine Learning Models | Presenting Author: Ahmed Saad