Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction - 2025 Workshop Presentation
This is a presentation on Real-Time Robust Adaptive Traffic Light System and Reservoir Engineering with Machine-Learning-Based Seismicity Forecasting and Data-Driven Ground Motion Prediction (RT Forecast) by Lawrence Berkeley National Laboratory, presented by Nori Nakata. This video slide presentation outlines the development of a near-real-time Adaptive Traffic Light System (ATLS) that combines machine-learning seismicity forecasting, generative AI ground-motion prediction, and high-pressure laboratory experiments to improve induced seismicity forecasting and reservoir engineering for Enhanced Geothermal Systems (EGS). This presentation was featured at the Utah FORGE R&D Annual Workshop on September 9, 2025. 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 Real-Time Robust Adaptive Traffic Light System and Reservoir Engineering with Machine-Learning-Based Seismicity Forecasting and Data-Driven Ground Motion Prediction (RT Forecast) by Lawrence Berkeley National Laboratory, presented by Nori Nakata. This video slide presentation outlines the development of a near-real-time Adaptive Traffic Light System (ATLS) that combines machine-learning seismicity forecasting, generative AI ground-motion prediction, and high-pressure laboratory experiments to improve induced seismicity forecasting and reservoir engineering for Enhanced Geothermal Systems (EGS). This presentation was featured at the Utah FORGE R&D Annual Workshop on September 9, 2025. 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 - Nakata, Nori
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Laboratory of the Rockies
DO -
KW - geothermal
KW - energy
KW - Utah FORGE
KW - 2025 Annual Workshop
KW - EGS
KW - induced seismicity
KW - traffic light system
KW - machine learning
KW - seismicity
KW - forecasting
KW - ground motion prediction
KW - generative AI
KW - reservoir engineering
KW - high-pressure experiments
KW - presentation
KW - presentation slides
KW - presentation recording
KW - report
LA - English
DA - 2025/09/18
PY - 2025
PB - Lawrence Berkeley National Laboratory
T1 - Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction - 2025 Workshop Presentation
UR - https://data.openei.org/submissions/8530
ER -
Nakata, Nori. Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction - 2025 Workshop Presentation. Lawrence Berkeley National Laboratory, 18 September, 2025, GDR. https://gdr.openei.org/submissions/1786.
Nakata, N. (2025). Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction - 2025 Workshop Presentation. [Data set]. GDR. Lawrence Berkeley National Laboratory. https://gdr.openei.org/submissions/1786
Nakata, Nori. Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction - 2025 Workshop Presentation. Lawrence Berkeley National Laboratory, September, 18, 2025. Distributed by GDR. https://gdr.openei.org/submissions/1786
@misc{OEDI_Dataset_8530,
title = {Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction - 2025 Workshop Presentation},
author = {Nakata, Nori},
abstractNote = {This is a presentation on Real-Time Robust Adaptive Traffic Light System and Reservoir Engineering with Machine-Learning-Based Seismicity Forecasting and Data-Driven Ground Motion Prediction (RT Forecast) by Lawrence Berkeley National Laboratory, presented by Nori Nakata. This video slide presentation outlines the development of a near-real-time Adaptive Traffic Light System (ATLS) that combines machine-learning seismicity forecasting, generative AI ground-motion prediction, and high-pressure laboratory experiments to improve induced seismicity forecasting and reservoir engineering for Enhanced Geothermal Systems (EGS). This presentation was featured at the Utah FORGE R\&D Annual Workshop on September 9, 2025. 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/1786},
year = {2025},
howpublished = {GDR, Lawrence Berkeley National Laboratory, https://gdr.openei.org/submissions/1786},
note = {Accessed: 2026-09-11}
}
Details
Data from Sep 18, 2025
Last updated Sep 21, 2025
Submitted Sep 18, 2025
Organization
Lawrence Berkeley National Laboratory
Contact
Nori Nakata
Authors
Original Source
https://gdr.openei.org/submissions/1786Research Areas
Keywords
geothermal, energy, Utah FORGE, 2025 Annual Workshop, EGS, induced seismicity, traffic light system, machine learning, seismicity, forecasting, ground motion prediction, generative AI, reservoir engineering, high-pressure experiments, presentation, presentation slides, presentation recording, reportDOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080
