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Utah FORGE 6-3629: Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation - 2024 Annual 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 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 in the Utah FORGE R&D Annual Workshop on August 15, 2024.
Complete Metadata
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|---|---|
| accessLevel | public |
| bureauCode |
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"019:20"
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| contactPoint |
{
"fn": "Sean Lattice",
"@type": "vcard:Contact",
"hasEmail": "mailto:slattis@egi.utah.edu"
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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 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 in the Utah FORGE R&D Annual Workshop on August 15, 2024. |
| distribution |
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{
"@type": "dcat:Distribution",
"title": "Presentation Recording.mp4",
"format": "mp4",
"accessURL": "https://gdr.openei.org/files/1652/UofU%206-3629%20GMT20240815-135917_Recording_as_1920x1080.mp4",
"mediaType": "application/octet-stream",
"description": "As part of the 2024 Utah FORGE R&D Workshop, this presentation offers the newest updates to the Cutting Edge Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation project from The University of Utah. The presentation follows a standard format, with a 15 minute presentation section followed by a 10 minute Q&A via Utah FORGE panelists and the presenters. "
}
]
|
| DOI | 10.15121/2441432 |
| identifier | https://data.openei.org/submissions/7722 |
| issued | 2024-09-15T06:00:00Z |
| keyword |
[
"AI",
"EGS",
"University of Utah",
"Utah FORGE",
"community saftey",
"data-driven decisions",
"energy",
"fracing",
"geothermal",
"ground motion",
"immediate response",
"improved saftey",
"infrastructure protection",
"machine learning",
"presentation",
"proactive risk mitigation",
"seismic",
"seismic hazards",
"stimulation",
"video"
]
|
| landingPage | https://gdr.openei.org/submissions/1652 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2024-09-17T15:57:44Z |
| programCode |
[
"019:006"
]
|
| projectLead | Lauren Boyd |
| projectNumber | EE0007080 |
| projectTitle | Utah FORGE |
| publisher |
{
"name": "Energy and Geoscience Institute at the University of Utah",
"@type": "org:Organization"
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|
| title | Utah FORGE 6-3629: Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation - 2024 Annual Workshop Presentation |
