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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.
Complete Metadata
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| contactPoint |
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"fn": "Sean Lattice",
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"hasEmail": "mailto:slattis@egi.utah.edu"
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| 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."
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|
| 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 |
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"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 2-2439v2: Characterizing In-Situ Stress with Laboratory Modelling and Field Measurements - 2024 Annual Workshop Presentation |
