Induced seismicity is both a valuable reservoir management tool and a significant challenge, particularly for enhanced geothermal system (EGS) projects. While carefully managed seismic activity can help create and maintain fluid pathways, large earthquakes—such as those that occurred at the Basel and Pohang EGS sites—have raised public concern and highlighted the risks of large-scale deployment.
Beyond avoiding damaging earthquakes, geothermal operators also aim to keep reservoir stimulation within a defined radius—typically a few hundred meters around the injection well—to maximize heat extraction from a controlled volume of rock. Effective seismic monitoring must therefore balance two key objectives: mitigating the risk of large events and optimizing reservoir performance.
Our research focuses on developing anadaptive traffic light system that combines seismicity forecasting, ground motion modeling, real-time observations, and machine-learning techniques. This integrated approach is designed to improve seismic hazard management. The system is being tested and calibrated using both natural seismicity and controlled experimental data.
Sponsored by: DOE-BES, Utah FORGE
ERL Personnel: Matej Pec, Hoagy O’Ghaffari, Ulrich Mok, Nori Nakata