Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions - September 2023 Report

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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 -
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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

Ayyaz Mustafa

University of Pittsburgh

Andrew Bunger

University of Pittsburgh

Mark Kelley

Battelle Memorial Institute

DOE Project Details

Project Name Utah FORGE

Project Lead Lauren Boyd

Project Number EE0007080

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