Civil and Environmental Engineering John and Marcia Price College of Engineering

While geothermal energy is a clean and sustainable power source, its integration on a larger scale presents challenges, including the need for advanced technology to manage geothermal reservoirs, mitigate seismic risks, and address site-specific limitations such as resource location and drilling depth. Additionally, the initial investment costs and the complexities of infrastructure development can hinder widespread adoption, making it essential to overcome these barriers to fully harness geothermal energy’s potential.

Dr. No’am Dvory, a Research Assistant Professor of Civil & Environmental Engineering, is leading groundbreaking efforts to revolutionize how seismic risks are managed in geothermal energy projects. His latest project is crucial for advancing enhanced geothermal systems globally, offering sustainable and potentially life-saving solutions.

Minimizing Seismic Risks in Energy Development with Machine Learning

Dr. Dvory has recently secured significant funding for an innovative project at the Utah FORGE site, collaborating with experts from the University of California, Berkeley, the University of Calgary, and Tel Aviv University. This $1,021,798 project integrates machine learning, geomechanics, and seismology to develop real-time decision-making tools for geothermal reservoir stimulation.

Geothermal reservoir stimulation—a technique needed to produce geothermal energy more efficiently— can potentially induce felt earthquakes, a challenge observed worldwide. Dr. Dvory’s project aims to mitigate this risk by creating a comprehensive, real-time framework that incorporates advanced scientific tools like earthquake source location, slip hazard estimation, and maximum earthquake magnitude forecasting.

The project will tackle global challenges in geothermal energy and seismic hazard management by:

• Enhancing machine-learning techniques for accurate seismic event location and magnitude estimation.
• Refining fault slip assessments through Bayesian uncertainty analysis.
• Integrating tools for predicting maximum earthquake magnitude.
• Upgrading current models to consider real-time parameter distributions for better damage and nuisance predictions.

The culmination of this work will be an interactive tool that continuously delivers risk assessments, reducing operational risks and enhancing the effectiveness of geothermal reservoir stimulation. While the likelihood of felt seismic events at the Utah FORGE site is low, the advancements from this project are vital for the global development of enhanced geothermal systems.

by Joe LaFata