Estimate stress for 16B(78)-32 based on sonic logging data and laboratory data, enabled by the validated ML algorithm
Report documenting completion of Milestone 2.3.2 of Utah FORGE project 2439v2: A Multi-Component Approach to Characterizing In-Situ Stress at the U.S. DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement
Presents: 1) Laboratory Triaxial Ultrasonic Velocity Experiments in 5 core samples from 16B(78)-32, 2) Machine Learning training to lab data, 3) Clustering by petrophysical properties from well logs to identify sections of wellbore from the same facies as the core samples, 4) Using trained model with sonic logging data for wave velocities in order to estimate vertical, maximum horizontal, and minimum horizontal stresses along 16B(78)-32.
Citation Formats
TY - DATA
AB - Report documenting completion of Milestone 2.3.2 of Utah FORGE project 2439v2: A Multi-Component Approach to Characterizing In-Situ Stress at the U.S. DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement
Presents: 1) Laboratory Triaxial Ultrasonic Velocity Experiments in 5 core samples from 16B(78)-32, 2) Machine Learning training to lab data, 3) Clustering by petrophysical properties from well logs to identify sections of wellbore from the same facies as the core samples, 4) Using trained model with sonic logging data for wave velocities in order to estimate vertical, maximum horizontal, and minimum horizontal stresses along 16B(78)-32.
AU - Mustafa, Ayyaz
A2 - Lu, Guanyi
A3 - Bunger, Andrew
DB - Geothermal Data Repository
DP - Open EI | National Renewable Energy Laboratory
DO -
KW - geothermal
KW - energy
KW - Utah
KW - 16B78-32
KW - In-situ Stress
KW - Ultrasonic Velocity
LA - English
DA - 2025/06/05
PY - 2025
PB - University of Pittsburgh - Pittsburgh, PA
T1 - Estimate stress for 16B(78)-32 based on sonic logging data and laboratory data, enabled by the validated ML algorithm
UR - https://gdr.openei.org/submissions/1743
ER -
Mustafa, Ayyaz, et al. Estimate stress for 16B(78)-32 based on sonic logging data and laboratory data, enabled by the validated ML algorithm . University of Pittsburgh - Pittsburgh, PA, 5 June, 2025, Geothermal Data Repository. https://gdr.openei.org/submissions/1743.
Mustafa, A., Lu, G., & Bunger, A. (2025). Estimate stress for 16B(78)-32 based on sonic logging data and laboratory data, enabled by the validated ML algorithm . [Data set]. Geothermal Data Repository. University of Pittsburgh - Pittsburgh, PA. https://gdr.openei.org/submissions/1743
Mustafa, Ayyaz, Guanyi Lu, and Andrew Bunger. Estimate stress for 16B(78)-32 based on sonic logging data and laboratory data, enabled by the validated ML algorithm . University of Pittsburgh - Pittsburgh, PA, June, 5, 2025. Distributed by Geothermal Data Repository. https://gdr.openei.org/submissions/1743
@misc{GDR_Dataset_1743,
title = {Estimate stress for 16B(78)-32 based on sonic logging data and laboratory data, enabled by the validated ML algorithm },
author = {Mustafa, Ayyaz and Lu, Guanyi and Bunger, Andrew},
abstractNote = {Report documenting completion of Milestone 2.3.2 of Utah FORGE project 2439v2: A Multi-Component Approach to Characterizing In-Situ Stress at the U.S. DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement
Presents: 1) Laboratory Triaxial Ultrasonic Velocity Experiments in 5 core samples from 16B(78)-32, 2) Machine Learning training to lab data, 3) Clustering by petrophysical properties from well logs to identify sections of wellbore from the same facies as the core samples, 4) Using trained model with sonic logging data for wave velocities in order to estimate vertical, maximum horizontal, and minimum horizontal stresses along 16B(78)-32.},
url = {https://gdr.openei.org/submissions/1743},
year = {2025},
howpublished = {Geothermal Data Repository, University of Pittsburgh - Pittsburgh, PA, https://gdr.openei.org/submissions/1743},
note = {Accessed: 2025-06-06}
}
Details
Data from Jun 5, 2025
Last updated Jun 5, 2025
Submitted Jun 5, 2025
Organization
University of Pittsburgh - Pittsburgh, PA
Contact
Andrew Bunger
412.624.9875
Authors
Keywords
geothermal, energy, Utah, 16B78-32, In-situ Stress, Ultrasonic VelocityDOE Project Details
Project Name A Multi-Component Approach to Characterizing In-Situ Stress at the U.S. DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement
Project Lead Lauren Boyd
Project Number EE0007080
