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Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions
This report reviews the training of machine learning algorithms to laboratory triaxial ultrasonic velocity data for Utah FORGE Well 16A(78)-32. Three machine learning (ML) predictive models were developed for the prediction of vertical and two orthogonally oriented horizontal stresses in the well. The ML models were trained using laboratory-based triaxial ultrasonic wave velocity (labTUV) data wherein wave velocities were measured with various combinations of true triaxial applied stress. The ultrasonic velocities data include compressional, fast shear, and slow shear velocities in each of three directions for a total of nine velocities for each stress combination. However, because the ultimate goal is to deploy the trained model for interpretation of field sonic log data where only the vertically propagating waves are measured, the work here focuses on just the wave velocities with vertical (z-direction) propagation. Also, because vertical (overburden) is often well constrained, one approach explored here is to take the vertical stress also as known and train the model to predict the two horizontal stresses. This work was done as part of Utah FORGE project 2439: A Multi-Component Approach to Characterizing In-Situ Stress at the U.S. DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement.
Complete Metadata
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|---|---|
| accessLevel | public |
| bureauCode |
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| contactPoint |
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"fn": "Mark Kelley",
"@type": "vcard:Contact",
"hasEmail": "mailto:kelleym@battelle.org"
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| dataQuality |
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| description | This report reviews the training of machine learning algorithms to laboratory triaxial ultrasonic velocity data for Utah FORGE Well 16A(78)-32. Three machine learning (ML) predictive models were developed for the prediction of vertical and two orthogonally oriented horizontal stresses in the well. The ML models were trained using laboratory-based triaxial ultrasonic wave velocity (labTUV) data wherein wave velocities were measured with various combinations of true triaxial applied stress. The ultrasonic velocities data include compressional, fast shear, and slow shear velocities in each of three directions for a total of nine velocities for each stress combination. However, because the ultimate goal is to deploy the trained model for interpretation of field sonic log data where only the vertically propagating waves are measured, the work here focuses on just the wave velocities with vertical (z-direction) propagation. Also, because vertical (overburden) is often well constrained, one approach explored here is to take the vertical stress also as known and train the model to predict the two horizontal stresses. This work was done as part of Utah FORGE project 2439: A Multi-Component Approach to Characterizing In-Situ Stress at the U.S. DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "Technical Report.pdf",
"format": "pdf",
"accessURL": "https://gdr.openei.org/files/1519/Battelle%20Project%202_2439%20Milestone%202.2.1.pdf",
"mediaType": "application/pdf",
"description": "This report includes a description of the machine learning modelling approach used, the results of that modelling, and the development of mathematical correlations for stress estimations."
}
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|
| identifier | https://data.openei.org/submissions/8508 |
| issued | 2023-06-19T06:00:00Z |
| keyword |
[
"Utah FORGE",
"artificial neural network",
"energy",
"feed forward artificial neural network",
"geophysics",
"geothermal",
"in-situ stress",
"machine learning",
"model",
"seismic",
"stress characterization",
"stress prediction",
"triaxial"
]
|
| landingPage | https://gdr.openei.org/submissions/1519 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2025-02-18T19:03:49Z |
| programCode |
[
"019:006"
]
|
| projectLead | Lauren Boyd |
| projectNumber | EE0007080 |
| projectTitle | Utah FORGE |
| publisher |
{
"name": "Battelle Memorial Institute",
"@type": "org:Organization"
}
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|
| title | Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions |
