Utah FORGE 6-3656: Real-Time Robust Adaptive Traffic Light System and Reservoir Engineering with Machine-Learning-Based Seismicity Forecasting and Data-Driven Ground Motion Prediction (RT Forecast) - 2026 Workshop Presentation

This is a presentation on Real-Time Robust Adaptive Traffic Light System and Reservoir Engineering with Machine-Learning-Based Seismicity Forecasting and Data-Driven Ground Motion Prediction (RT Forecast) by Lawrence Berkeley National Laboratory, presented by Dr. Nori Nakata. The project's objective is to develop an adaptive traffic light system for Enhanced Geothermal Systems (EGS) operations by integrating machine-learning-based seismicity forecasting, laboratory rock deformation experiments, and data-driven ground motion prediction models. This presentation highlights research focused on improving induced seismicity forecasting, understanding fracture and rupture behavior, and advancing risk-informed reservoir management strategies for geothermal development. This presentation was featured at the Utah FORGE R&D Annual Workshop, held September 30-October 1, 2026. The workshop offered a valuable opportunity to review the progress of Research and Development projects funded under Solicitation 2022-2, which aim to improve our understanding of the key factors influencing Enhanced Geothermal System (EGS) reservoir and resource development.