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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs

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Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulations have been conducted in TETRAD-G and CMG STARS to explore different injection and production fluid flow rates and allocations and to develop a training data set for ML. This process included simulating the historical injection and production since 1979 and prediction of future performance through 2040. ML networks were created and trained using TensorFlow based on multilayer perceptron, long short-term memory, and convolutional neural network architectures. These networks took as input selected flow rates, injection temperatures, and historical field operation data and produced estimates of future production temperatures. This approach was first successfully tested on a simplified single-fracture doublet system, followed by the application to the BHS reservoir. Using an initial BHS data set with 37 simulated scenarios, the trained and validated network predicted the production temperature for six production wells with the mean absolute percentage error of less than 8%. In a complementary analysis effort, the principal component analysis applied to 13 BHS geological parameters revealed that vertical fracture permeability shows the strongest correlation with fault density and fault intersection density. A new BHS reservoir model was developed considering the fault intersection density as proxy for permeability. This new reservoir model helps to explore underexploited zones in the reservoir. A data gathering plan to obtain additional subsurface data was developed; it includes temperature surveying for three idle injection wells at which the reservoir simulations indicate high bottom-hole temperatures. The collected data assist with calibrating the reservoir model. Data gathering activities are planned for the first quarter of 2021.

This GDR submission includes a preprint of the paper titled "Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs" presented at the 46th Stanford Geothermal Workshop (SGW) on Geothermal Reservoir Engineering from February 16-18, 2021.

Citation Formats

National Renewable Energy Laboratory. (2021). Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs [data set]. Retrieved from https://gdr.openei.org/submissions/1300.
Export Citation to RIS
Beckers, Koenraad F., Duplyakin, Dmitry, Martin, Michael J., Johnston, Henry E., and Siler, Drew L. Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs. United States: N.p., 18 Feb, 2021. Web. https://gdr.openei.org/submissions/1300.
Beckers, Koenraad F., Duplyakin, Dmitry, Martin, Michael J., Johnston, Henry E., & Siler, Drew L. Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs. United States. https://gdr.openei.org/submissions/1300
Beckers, Koenraad F., Duplyakin, Dmitry, Martin, Michael J., Johnston, Henry E., and Siler, Drew L. 2021. "Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs". United States. https://gdr.openei.org/submissions/1300.
@div{oedi_4064, title = {Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs}, author = {Beckers, Koenraad F., Duplyakin, Dmitry, Martin, Michael J., Johnston, Henry E., and Siler, Drew L.}, abstractNote = {Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulations have been conducted in TETRAD-G and CMG STARS to explore different injection and production fluid flow rates and allocations and to develop a training data set for ML. This process included simulating the historical injection and production since 1979 and prediction of future performance through 2040. ML networks were created and trained using TensorFlow based on multilayer perceptron, long short-term memory, and convolutional neural network architectures. These networks took as input selected flow rates, injection temperatures, and historical field operation data and produced estimates of future production temperatures. This approach was first successfully tested on a simplified single-fracture doublet system, followed by the application to the BHS reservoir. Using an initial BHS data set with 37 simulated scenarios, the trained and validated network predicted the production temperature for six production wells with the mean absolute percentage error of less than 8%. In a complementary analysis effort, the principal component analysis applied to 13 BHS geological parameters revealed that vertical fracture permeability shows the strongest correlation with fault density and fault intersection density. A new BHS reservoir model was developed considering the fault intersection density as proxy for permeability. This new reservoir model helps to explore underexploited zones in the reservoir. A data gathering plan to obtain additional subsurface data was developed; it includes temperature surveying for three idle injection wells at which the reservoir simulations indicate high bottom-hole temperatures. The collected data assist with calibrating the reservoir model. Data gathering activities are planned for the first quarter of 2021.

This GDR submission includes a preprint of the paper titled "Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs" presented at the 46th Stanford Geothermal Workshop (SGW) on Geothermal Reservoir Engineering from February 16-18, 2021. }, doi = {}, url = {https://gdr.openei.org/submissions/1300}, journal = {}, number = , volume = , place = {United States}, year = {2021}, month = {02}}

Details

Data from Feb 18, 2021

Last updated Nov 8, 2021

Submitted Apr 12, 2021

Organization

National Renewable Energy Laboratory

Contact

Koenraad Beckers

Authors

Koenraad F. Beckers

National Renewable Energy Laboratory

Dmitry Duplyakin

National Renewable Energy Laboratory

Michael J. Martin

National Renewable Energy Laboratory

Henry E. Johnston

National Renewable Energy Laboratory

Drew L. Siler

United States Geological Survey

Research Areas

DOE Project Details

Project Name Insightful Subsurface Characterizations and Predictions

Project Lead Mike Weathers

Project Number 35517

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