Related Datasets

Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2026 Workshop Presentation

Publicly accessible License

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.

6-3712 GTC 2026 Annual Workshop Presentation.mp4

Recording of the 2026 Annual Workshop presentation in .mp4 format. This gives details of the estimation model for seismic responses due to stimulation.

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6-3712-GTC Annual Workshop Presentation.pdf

2026 Annual Workshop presentation slides. This gives details of the estimation model for seismic responses due to stimulation.

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6-3712-GTC-2026 Annual Report.pdf

2026 Annual Workshop report in .pdf format. This gives project overview and technical details of the estimation model for seismic responses due to stimulation.

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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  - Open Energy Data Initiative (OEDI)
DP  - Open EI | National Laboratory of the Rockies
DO  - 
KW  - geothermal
KW  - energy
KW  - Utah FORGE
KW  - EGS
KW  - 2026 Annual Workshop
KW  - machine-learning
KW  - neural networks
KW  - physics based
KW  - physics-informed
KW  - seismic response
KW  - induced seismicity
KW  - forecasting
KW  - stimulation-induced seismicity
KW  - traffic light
KW  - geothermal reservoir
KW  - reservoir management
KW  - presentation
KW  - report
KW  - decision support
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://data.openei.org/submissions/10244
ER  -

Export Citation to RIS

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, GDR. 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]. GDR. 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 GDR. https://gdr.openei.org/submissions/1883

@misc{OEDI_Dataset_10244,
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 = {GDR, GTC Analytics, https://gdr.openei.org/submissions/1883},
note = {Accessed: 2026-10-23}
}

Details

Data from Sep 30, 2026

Last updated Oct 22, 2026

Submitted Oct 20, 2026

Organization

GTC Analytics

Contact

Jesse Williams

Authors

Jesse Williams

GTC Analytics

Original Source

https://gdr.openei.org/submissions/1883

Research Areas

Geothermal Energy

Keywords

geothermal, energy, Utah FORGE, EGS, 2026 Annual Workshop, machine-learning, neural networks, physics based, physics-informed, seismic response, induced seismicity, forecasting, stimulation-induced seismicity, traffic light, geothermal reservoir, reservoir management, presentation, report, decision support

DOE Project Details

Project Name Utah FORGE

Project Lead Lauren Boyd

Project Number EE0007080