Utah FORGE 6-3629: Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation - 2025 Workshop Presentation
This is a presentation on the Cutting Edge Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation by the University of Utah, presented by Dr. No'am Zach Dvory. This video slide presentation, by the University of Utah, discussed the technical objectives of developing a real-time decision-making platform to enhance seismic monitoring and risk management during stimulation activities. 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.
Resources
3 resources available
-
6-3629 - 2025 Annual Report.pdf
PDF -
Presentation Slides.pdf
PDF -
Presentation Recording.mp4
MP4
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "019:20" ] |
| contactPoint |
{ "fn": "No'am Dvory", "@type": "vcard:Contact", "hasEmail": "mailto:nzd@egi.utah.edu" } |
| dataQuality |
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| description | This is a presentation on the Cutting Edge Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation by the University of Utah, presented by Dr. No'am Zach Dvory. This video slide presentation, by the University of Utah, discussed the technical objectives of developing a real-time decision-making platform to enhance seismic monitoring and risk management during stimulation activities. 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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| identifier | https://data.openei.org/submissions/8528 |
| issued | 2025-09-18T06:00:00Z |
| keyword |
[ "2025 Annual Workshop", "EGS", "Utah FORGE", "energy", "geomechanics", "geothermal", "machine learning", "presentation", "presentation recording", "presentation slides", "real-time decision making", "report", "seismic monitoring", "seismology", "stimulation risk management" ] |
| landingPage | https://gdr.openei.org/submissions/1784 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2025-09-21T20:19:53Z |
| programCode |
[ "019:006" ] |
| projectLead | Lauren Boyd |
| projectNumber | EE0007080 |
| projectTitle | Utah FORGE |
| publisher |
{ "name": "University of Utah", "@type": "org:Organization" } |
| spatial |
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|
| title | Utah FORGE 6-3629: Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation - 2025 Workshop Presentation |
