Utah FORGE 2-2439v2: Report on Predicting Far-Field Stresses Using Finite Element Modeling and Near-Wellbore Machine Learning for Well 16A(78)-32
This report presents the far-field stress predictions at two locations along the vertical section of Utah FORGE Well 16A (78)-32 using a physics-based thermo-poro-mechanical model. Three principal stresses in far-field were obtained by solving an inverse problem based on the near-wellbore stress estimates generated by the Machine Learning (ML) predictive model presented in a previous report, which is linked below as "Machine Learning for Well 16A(78)-32 Stress Predictions". Combined ML and physics-based Finite Element model was applied to translate the near-field stresses to stresses away from the wellbore/cooling-influenced zone. The thermo-poro-mechanical effect by pre-cooling circulation prior to well logging in an enhanced geothermal system (EGS) well was accounted for in the stress predictions at Well 16A (78)-32.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"019:20"
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| contactPoint |
{
"fn": "Andrew Bunger",
"@type": "vcard:Contact",
"hasEmail": "mailto:bunger@pitt.edu"
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| dataQuality |
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| description | This report presents the far-field stress predictions at two locations along the vertical section of Utah FORGE Well 16A (78)-32 using a physics-based thermo-poro-mechanical model. Three principal stresses in far-field were obtained by solving an inverse problem based on the near-wellbore stress estimates generated by the Machine Learning (ML) predictive model presented in a previous report, which is linked below as "Machine Learning for Well 16A(78)-32 Stress Predictions". Combined ML and physics-based Finite Element model was applied to translate the near-field stresses to stresses away from the wellbore/cooling-influenced zone. The thermo-poro-mechanical effect by pre-cooling circulation prior to well logging in an enhanced geothermal system (EGS) well was accounted for in the stress predictions at Well 16A (78)-32. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "August 2024 Report.pdf",
"format": "pdf",
"accessURL": "https://gdr.openei.org/files/1641/2-2439%20v2%20-%20University%20of%20Pittsburgh%20-%20Milestone%204.2.1%20Report_Submission.pdf",
"mediaType": "application/pdf",
"description": "This report documents the completion of a phase of Utah FORGE Project 2-2439v2: A Multi-Component Approach to Characterizing In-Situ Stress at the U.S. DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement. The report is entitled "Predicting Far-Field Stresses using Finite Element Model Based on Near-Wellbore Machine Learning Estimates for Well 16A(78)-32 ". The report offers a description of the project phase, as well as the methods, results, and conclusions."
},
{
"@type": "dcat:Distribution",
"title": "Machine Learning for Well 16A78-32 Stress Predictions",
"format": "HTML",
"accessURL": "https://gdr.openei.org/submissions/1593",
"mediaType": "text/html",
"description": "This preceding report documents the development and implementation of machine learning (ML) models for the prediction of in-situ vertical (Sv), minimum horizontal (SHmin) and maximum horizontal (SHmax) stresses in well 16A(78)-32. The detailed description of the experimental work was documented in a previous task report, which is linked below as "December 2022 Report". This 2023 task competition follows the accomplishments outlined the June 2023 report (also linked below), which elaborated the ML model development and validation strategy comprehensively. At this stage, prediction performances of ML models are further improved and implemented carefully for the estimation of in-situ stresses (i.e., Sv, SHmin, and SHmax over the depth ranging from 5000 to 6000 feet in the well 16A(78)-32). A comparison between ML-based and field-based stresses reflected the excellent harmony in terms of nominal errors at the sampling depths."
}
]
|
| identifier | https://data.openei.org/submissions/7711 |
| issued | 2024-08-30T06:00:00Z |
| keyword |
[
"16A78-32",
"2-2439v2",
"EGS",
"FEM",
"ML",
"Utah FORGE",
"energy",
"far-field",
"finite element method",
"geothermal",
"in-situ stress estimation",
"machine learning",
"machine learning model",
"physics-based modeling",
"pre-cooling",
"principal stress",
"report",
"stress prediction",
"technical report",
"thermo-poro-mechanical effect",
"velocity-to-stress relationship",
"well logging"
]
|
| landingPage | https://gdr.openei.org/submissions/1641 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2024-09-05T15:37:53Z |
| programCode |
[
"019:006"
]
|
| projectLead | Lauren Boyd |
| projectNumber | EE0007080 |
| projectTitle | Utah FORGE |
| publisher |
{
"name": "University of Pittsburgh",
"@type": "org:Organization"
}
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| title | Utah FORGE 2-2439v2: Report on Predicting Far-Field Stresses Using Finite Element Modeling and Near-Wellbore Machine Learning for Well 16A(78)-32 |
