Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions - September 2023 Report
This task completion report documents the development and implementation of machine learning (ML) models for the prediction of in-situ vertical (Sv), minimum horizontal (SHmin) and maximum horizontal (SHmax) stresses in well 16A(78)-32. The detailed description of the experimental work was documented in a previous task report, which is linked below as "December 2022 Report". This 2023 task competition follows the accomplishments outlined the June 2023 report (also linked below), which elaborated the ML model development and validation strategy comprehensively. At this stage, prediction performances of ML models are further improved and implemented carefully for the estimation of in-situ stresses (i.e., Sv, SHmin, and SHmax over the depth ranging from 5000 to 6000 feet in the well 16A(78)-32). A comparison between ML-based and field-based stresses reflected the excellent harmony in terms of nominal errors at the sampling depths.
Citation Formats
TY - DATA
AB - This task completion report documents the development and implementation of machine learning (ML) models for the prediction of in-situ vertical (Sv), minimum horizontal (SHmin) and maximum horizontal (SHmax) stresses in well 16A(78)-32. The detailed description of the experimental work was documented in a previous task report, which is linked below as "December 2022 Report". This 2023 task competition follows the accomplishments outlined the June 2023 report (also linked below), which elaborated the ML model development and validation strategy comprehensively. At this stage, prediction performances of ML models are further improved and implemented carefully for the estimation of in-situ stresses (i.e., Sv, SHmin, and SHmax over the depth ranging from 5000 to 6000 feet in the well 16A(78)-32). A comparison between ML-based and field-based stresses reflected the excellent harmony in terms of nominal errors at the sampling depths.
AU - Mustafa, Ayyaz
A2 - Bunger, Andrew
A3 - Kelley, Mark
DB - Geothermal Data Repository
DP - Open EI | National Renewable Energy Laboratory
DO -
KW - geothermal
KW - energy
KW - Utah
KW - FORGE
KW - geomechanics
KW - machine learning
KW - FFNN
KW - artificial neural network
KW - ANN
KW - in-situ stress
KW - stress characterization
KW - labTUV
KW - triaxial
KW - stress
KW - feed forward artificial neural network
KW - ML
KW - EGS
KW - modelling
KW - exploratory data analysis
KW - EDA
LA - English
DA - 2023/09/28
PY - 2023
PB - Battelle Memorial Institute
T1 - Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions - September 2023 Report
UR - https://gdr.openei.org/submissions/1593
ER -
Mustafa, Ayyaz, et al. Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions - September 2023 Report. Battelle Memorial Institute, 28 September, 2023, Geothermal Data Repository. https://gdr.openei.org/submissions/1593.
Mustafa, A., Bunger, A., & Kelley, M. (2023). Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions - September 2023 Report. [Data set]. Geothermal Data Repository. Battelle Memorial Institute. https://gdr.openei.org/submissions/1593
Mustafa, Ayyaz, Andrew Bunger, and Mark Kelley. Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions - September 2023 Report. Battelle Memorial Institute, September, 28, 2023. Distributed by Geothermal Data Repository. https://gdr.openei.org/submissions/1593
@misc{GDR_Dataset_1593,
title = {Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions - September 2023 Report},
author = {Mustafa, Ayyaz and Bunger, Andrew and Kelley, Mark},
abstractNote = {This task completion report documents the development and implementation of machine learning (ML) models for the prediction of in-situ vertical (Sv), minimum horizontal (SHmin) and maximum horizontal (SHmax) stresses in well 16A(78)-32. The detailed description of the experimental work was documented in a previous task report, which is linked below as "December 2022 Report". This 2023 task competition follows the accomplishments outlined the June 2023 report (also linked below), which elaborated the ML model development and validation strategy comprehensively. At this stage, prediction performances of ML models are further improved and implemented carefully for the estimation of in-situ stresses (i.e., Sv, SHmin, and SHmax over the depth ranging from 5000 to 6000 feet in the well 16A(78)-32). A comparison between ML-based and field-based stresses reflected the excellent harmony in terms of nominal errors at the sampling depths.},
url = {https://gdr.openei.org/submissions/1593},
year = {2023},
howpublished = {Geothermal Data Repository, Battelle Memorial Institute, https://gdr.openei.org/submissions/1593},
note = {Accessed: 2025-04-24}
}
Details
Data from Sep 28, 2023
Last updated Aug 22, 2024
Submitted Apr 1, 2024
Organization
Battelle Memorial Institute
Contact
Sanjay Mawalkar
614.424.7593
Authors
Keywords
geothermal, energy, Utah, FORGE, geomechanics, machine learning, FFNN, artificial neural network, ANN, in-situ stress, stress characterization, labTUV, triaxial, stress, feed forward artificial neural network, ML, EGS, modelling, exploratory data analysis, EDADOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080