Utah FORGE 2-2439v2: Predicting Far-Field Stresses using Finite Element Model Based on Near-Wellbore Machine Learning Estimates for Well 16A(78)-32
This report presents the far-field stress predictions at two locations along the vertical section of 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 task 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
University of Pittsburgh. (2024). Utah FORGE 2-2439v2: Predicting Far-Field Stresses using Finite Element Model Based on Near-Wellbore Machine Learning Estimates for Well 16A(78)-32 [data set]. Retrieved from https://gdr.openei.org/submissions/1641.
Lu, Guanyi, Mustafa, Ayyaz, and Bunger, Andrew. Utah FORGE 2-2439v2: Predicting Far-Field Stresses using Finite Element Model Based on Near-Wellbore Machine Learning Estimates for Well 16A(78)-32. United States: N.p., 30 Aug, 2024. Web. https://gdr.openei.org/submissions/1641.
Lu, Guanyi, Mustafa, Ayyaz, & Bunger, Andrew. Utah FORGE 2-2439v2: Predicting Far-Field Stresses using Finite Element Model Based on Near-Wellbore Machine Learning Estimates for Well 16A(78)-32. United States. https://gdr.openei.org/submissions/1641
Lu, Guanyi, Mustafa, Ayyaz, and Bunger, Andrew. 2024. "Utah FORGE 2-2439v2: Predicting Far-Field Stresses using Finite Element Model Based on Near-Wellbore Machine Learning Estimates for Well 16A(78)-32". United States. https://gdr.openei.org/submissions/1641.
@div{oedi_1641, title = {Utah FORGE 2-2439v2: Predicting Far-Field Stresses using Finite Element Model Based on Near-Wellbore Machine Learning Estimates for Well 16A(78)-32}, author = {Lu, Guanyi, Mustafa, Ayyaz, and Bunger, Andrew.}, abstractNote = {This report presents the far-field stress predictions at two locations along the vertical section of 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 task 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.}, doi = {}, url = {https://gdr.openei.org/submissions/1641}, journal = {}, number = , volume = , place = {United States}, year = {2024}, month = {08}}
Details
Data from Aug 30, 2024
Last updated Sep 4, 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 relationshipDOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080