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Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions - September 2023 Report
This task completion 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.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"019:20"
]
|
| contactPoint |
{
"fn": "Sanjay Mawalkar",
"@type": "vcard:Contact",
"hasEmail": "mailto:mawalkar@battelle.org"
}
|
| dataQuality |
true
|
| description | This task completion 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. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "September 2023 Report - MOST RECENT.pdf",
"format": "pdf",
"accessURL": "https://gdr.openei.org/files/1593/Battelle%20Project%202_2439%20Milestone%202.3.1%20Report.pdf",
"mediaType": "application/pdf",
"description": "This is the current report, documenting the most recent task completion. The report documents the application of machine learning models trained on labTUV data for predicting vertical and horizontal stresses from field sonic-log data. "
},
{
"@type": "dcat:Distribution",
"title": "December 2022 Report",
"format": "HTML",
"accessURL": "https://gdr.openei.org/submissions/1438",
"mediaType": "text/html",
"description": "This GDR link contains the preceding December 2022 report referenced above. That report is in the Core-Based In-Situ Stress Estimation for Utah FORGE Well 16A78-32.pdf resource. This dataset also house the laboratory data that the ML learning models were trained on for this project."
},
{
"@type": "dcat:Distribution",
"title": "June 2023 Report",
"format": "HTML",
"accessURL": "https://gdr.openei.org/submissions/1519",
"mediaType": "text/html",
"description": "This GDR submission contains the preceding June 2023 report referenced above."
}
]
|
| identifier | https://data.openei.org/submissions/7670 |
| issued | 2023-09-28T06:00:00Z |
| keyword |
[
"ANN",
"EDA",
"EGS",
"FFNN",
"FORGE",
"ML",
"Utah",
"artificial neural network",
"energy",
"exploratory data analysis",
"feed forward artificial neural network",
"geomechanics",
"geothermal",
"in-situ stress",
"labTUV",
"machine learning",
"modelling",
"stress",
"stress characterization",
"triaxial"
]
|
| landingPage | https://gdr.openei.org/submissions/1593 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2024-08-22T17:09:02Z |
| programCode |
[
"019:006"
]
|
| projectLead | Lauren Boyd |
| projectNumber | EE0007080 |
| projectTitle | Utah FORGE |
| publisher |
{
"name": "Battelle Memorial Institute",
"@type": "org:Organization"
}
|
| spatial |
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|
| title | Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions - September 2023 Report |
