EGS-Twin is designed to deliver real-time insight into subsurface behavior and operational performance through the integration of high-resolution field data with physics-based modeling and AI-driven forecasting. To build EGS-Twin, PNNL researchers will use Fervo’s industry expertise and field data to train scalable AI models on NVIDIA AI infrastructure. The trained AI models will then be integrated into the NVIDIA Omniverse libraries, helping geothermal operators more quickly identify and respond to subsurface changes, optimize power generation, and strengthen the scalability of enhanced geothermal systems.
“We believe that digital twins will expedite the learning curve for geothermal development as we build and operate our GeoBlock assets,” said Fervo’s CTO and co-founder, Jack Norbeck. “Integrating high-fidelity physics-based models with AI-driven forecasting has the potential to reshape reservoir management, improve heat recovery, and enhance system reliability.”
PNNL will develop the workflows and data pipelines, leveraging high-performance computing, including U.S. Department of Energy supercomputing resources, to run large-scale simulations. Using currently available proprietary field data from Fervo’s Nevada and Utah sites, the PNNL team will begin training the digital twin immediately and continue refining the platform as additional production data comes online, with the platform scheduled for implementation by 2029.
