Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2026 Workshop Presentation
This is a presentation on Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks by GTC Analytics (Global Technology Connection), presented by Dr. Jesse Williams. The project is developing machine-learning and physics-informed models to improve forecasting of stimulation-induced seismicity at the Utah FORGE site and support enhanced geothermal reservoir management. 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.
Citation Formats
TY - DATA
AB - This is a presentation on Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks by GTC Analytics (Global Technology Connection), presented by Dr. Jesse Williams. The project is developing machine-learning and physics-informed models to improve forecasting of stimulation-induced seismicity at the Utah FORGE site and support enhanced geothermal reservoir management. 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.
AU - Williams, Jesse
DB - Geothermal Data Repository
DP - Open EI | National Laboratory of the Rockies
DO -
KW - geothermal
KW - energy
LA - English
DA - 2026/09/30
PY - 2026
PB - GTC Analytics
T1 - Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2026 Workshop Presentation
UR - https://gdr.openei.org/submissions/1883
ER -
Williams, Jesse. Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2026 Workshop Presentation. GTC Analytics, 30 September, 2026, Geothermal Data Repository. https://gdr.openei.org/submissions/1883.
Williams, J. (2026). Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2026 Workshop Presentation. [Data set]. Geothermal Data Repository. GTC Analytics. https://gdr.openei.org/submissions/1883
Williams, Jesse. Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2026 Workshop Presentation. GTC Analytics, September, 30, 2026. Distributed by Geothermal Data Repository. https://gdr.openei.org/submissions/1883
@misc{GDR_Dataset_1883,
title = {Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2026 Workshop Presentation},
author = {Williams, Jesse},
abstractNote = {This is a presentation on Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks by GTC Analytics (Global Technology Connection), presented by Dr. Jesse Williams. The project is developing machine-learning and physics-informed models to improve forecasting of stimulation-induced seismicity at the Utah FORGE site and support enhanced geothermal reservoir management. 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.},
url = {https://gdr.openei.org/submissions/1883},
year = {2026},
howpublished = {Geothermal Data Repository, GTC Analytics, https://gdr.openei.org/submissions/1883},
note = {Accessed: 2026-10-21}
}
Details
Data from Sep 30, 2026
Last updated Oct 20, 2026
Submitted Oct 20, 2026
Organization
GTC Analytics
Contact
Jesse Williams
Authors
Keywords
geothermal, energyDOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080
