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.
Complete Metadata
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| description | 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. |
| distribution |
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"title": "6-3656 - 2025 Annual Report.pdf",
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"accessURL": "https://gdr.openei.org/files/1786/6-3656-LBNL%202025%20Annual%20Report.pdf",
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"description": "This 2025 report summarizes the progress of the Utah FORGE project 6-3656."
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"description": "These are the slides presented at the 2025 Utah FORGE annual workshop for project 6-3656."
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| identifier | https://data.openei.org/submissions/8530 |
| issued | 2025-09-18T06:00:00Z |
| keyword |
[
"2025 Annual Workshop",
"EGS",
"Utah FORGE",
"energy",
"forecasting",
"generative AI",
"geothermal",
"ground motion prediction",
"high-pressure experiments",
"induced seismicity",
"machine learning",
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"reservoir engineering",
"seismicity",
"traffic light system"
]
|
| landingPage | https://gdr.openei.org/submissions/1786 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2025-09-21T20:38:55Z |
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| projectLead | Lauren Boyd |
| projectNumber | EE0007080 |
| projectTitle | Utah FORGE |
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"name": "Lawrence Berkeley National Laboratory",
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| title | Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction - 2025 Workshop Presentation |
