Utah FORGE 2-2439v2: Characterizing In-Situ Stress with Laboratory Modelling and Field Measurements - 2024 Annual Workshop Presentation
This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements project by The University of Pittsburgh, presented by Andrew Bunger. The project characterizes the stress in the Utah FORGE EGS reservoir using three methods:
Method 1: Demonstrate complimentary laboratory rock-core stress estimation combined with Machine Learning approach for measuring in-situ stress from field sonic log data;
Method 2: Complete field based in-situ measurement (mini-frac); and
Method 3: Develop a mechanics-based method for connection near wellbore stress measurements to stresses away from the well-bore.
This presentation was featured in the Utah FORGE R&D Annual Workshop on August 14, 2024.
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "019:20" ] |
| contactPoint |
{ "fn": "Sean Lattice", "@type": "vcard:Contact", "hasEmail": "mailto:slattis@egi.utah.edu" } |
| dataQuality |
true
|
| description | This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements project by The University of Pittsburgh, presented by Andrew Bunger. The project characterizes the stress in the Utah FORGE EGS reservoir using three methods: Method 1: Demonstrate complimentary laboratory rock-core stress estimation combined with Machine Learning approach for measuring in-situ stress from field sonic log data; Method 2: Complete field based in-situ measurement (mini-frac); and Method 3: Develop a mechanics-based method for connection near wellbore stress measurements to stresses away from the well-bore. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 14, 2024. |
| distribution |
[ { "@type": "dcat:Distribution", "title": "Presentation Recording.mp4", "format": "mp4", "accessURL": "https://gdr.openei.org/files/1640/PITTU%202-2439v2%20GMT20240814-185919_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 A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements project from The University of Pittsburgh. The presentation follows a standard format, with a 20 minute presentation section followed by a 25 minute Q&A via Utah FORGE panelists and the presenters." } ] |
| DOI | 10.15121/2439748 |
| identifier | https://data.openei.org/submissions/7710 |
| issued | 2024-09-04T06:00:00Z |
| keyword |
[ "Machine Learning", "Machine Learning for in-situ stress", "Utah FORGE", "energy", "geothermal", "in-situ stress", "mini-frac", "presentation", "rock mechanics", "rock stress", "sonic logs", "stress", "stress estimation", "video" ] |
| landingPage | https://gdr.openei.org/submissions/1640 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2024-09-06T17:37:10Z |
| 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" } |
| spatial |
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|
| title | Utah FORGE 2-2439v2: Characterizing In-Situ Stress with Laboratory Modelling and Field Measurements - 2024 Annual Workshop Presentation |
