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.
Citation Formats
TY - DATA
AB - 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.
AU - Lu, Guanyi
A2 - Mustafa, Ayyaz
A3 - Bunger, Andrew
DB - Geothermal Data Repository
DP - Open EI | National Renewable Energy Laboratory
DO -
KW - geothermal
KW - energy
KW - Utah FORGE
KW - in-situ stress estimation
KW - physics-based modeling
KW - finite element method
KW - machine learning model
KW - thermo-poro-mechanical effect
KW - well logging
KW - velocity-to-stress relationship
KW - machine learning
KW - FEM
KW - report
KW - technical report
KW - 16A78-32
KW - ML
KW - EGS
KW - 2-2439v2
KW - principal stress
KW - stress prediction
KW - far-field
KW - pre-cooling
LA - English
DA - 2024/08/30
PY - 2024
PB - University of Pittsburgh
T1 - Utah FORGE 2-2439v2: Report on Predicting Far-Field Stresses Using Finite Element Modeling and Near-Wellbore Machine Learning for Well 16A(78)-32
UR - https://gdr.openei.org/submissions/1641
ER -
Lu, Guanyi, et al. Utah FORGE 2-2439v2: Report on Predicting Far-Field Stresses Using Finite Element Modeling and Near-Wellbore Machine Learning for Well 16A(78)-32. University of Pittsburgh, 30 August, 2024, Geothermal Data Repository. https://gdr.openei.org/submissions/1641.
Lu, G., Mustafa, A., & Bunger, A. (2024). Utah FORGE 2-2439v2: Report on Predicting Far-Field Stresses Using Finite Element Modeling and Near-Wellbore Machine Learning for Well 16A(78)-32. [Data set]. Geothermal Data Repository. University of Pittsburgh. https://gdr.openei.org/submissions/1641
Lu, Guanyi, Ayyaz Mustafa, and Andrew Bunger. Utah FORGE 2-2439v2: Report on Predicting Far-Field Stresses Using Finite Element Modeling and Near-Wellbore Machine Learning for Well 16A(78)-32. University of Pittsburgh, August, 30, 2024. Distributed by Geothermal Data Repository. https://gdr.openei.org/submissions/1641
@misc{GDR_Dataset_1641,
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},
author = {Lu, Guanyi and Mustafa, Ayyaz and Bunger, Andrew},
abstractNote = {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.},
url = {https://gdr.openei.org/submissions/1641},
year = {2024},
howpublished = {Geothermal Data Repository, University of Pittsburgh, https://gdr.openei.org/submissions/1641},
note = {Accessed: 2025-04-24}
}
Details
Data from Aug 30, 2024
Last updated Sep 5, 2024
Submitted Sep 4, 2024
Organization
University of Pittsburgh
Contact
Andrew Bunger
412.624.9875
Authors
Keywords
geothermal, energy, Utah FORGE, in-situ stress estimation, physics-based modeling, finite element method, machine learning model, thermo-poro-mechanical effect, well logging, velocity-to-stress relationship, machine learning, FEM, report, technical report, 16A78-32, ML, EGS, 2-2439v2, principal stress, stress prediction, far-field, pre-coolingDOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080
