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Utah FORGE Project 2439: Machine Learning for Well 16A(78)-32 Stress Predictions

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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.

Citation Formats

Battelle Memorial Institute. (2023). Utah FORGE Project 2439: Machine Learning for Well 16A(78)-32 Stress Predictions [data set]. Retrieved from https://gdr.openei.org/submissions/1519.
Export Citation to RIS
Kelley, Mark, Mustafa, Ayyaz, and Bunger, Andrew. Utah FORGE Project 2439: Machine Learning for Well 16A(78)-32 Stress Predictions. United States: N.p., 19 Jun, 2023. Web. https://gdr.openei.org/submissions/1519.
Kelley, Mark, Mustafa, Ayyaz, & Bunger, Andrew. Utah FORGE Project 2439: Machine Learning for Well 16A(78)-32 Stress Predictions. United States. https://gdr.openei.org/submissions/1519
Kelley, Mark, Mustafa, Ayyaz, and Bunger, Andrew. 2023. "Utah FORGE Project 2439: Machine Learning for Well 16A(78)-32 Stress Predictions". United States. https://gdr.openei.org/submissions/1519.
@div{oedi_5956, title = {Utah FORGE Project 2439: Machine Learning for Well 16A(78)-32 Stress Predictions}, author = {Kelley, Mark, Mustafa, Ayyaz, and Bunger, Andrew.}, abstractNote = {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. }, doi = {}, url = {https://gdr.openei.org/submissions/1519}, journal = {}, number = , volume = , place = {United States}, year = {2023}, month = {06}}

Details

Data from Jun 19, 2023

Last updated Sep 14, 2023

Submitted Jul 12, 2023

Organization

Battelle Memorial Institute

Contact

Mark Kelley

614.424.3704

Authors

Mark Kelley

Battelle Memorial Institute

Ayyaz Mustafa

University of Pittsburgh

Andrew Bunger

University of Pittsburgh

Research Areas

DOE Project Details

Project Name Enhanced Geothermal System Concept Testing and Development at the Milford City, Utah Forge Site

Project Lead Lauren Boyd

Project Number EE0007080

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