Subsurface characterization and machine learning predictions at Brady Hot Springs

Koenraad F. Beckers; Dmitry Duplyakin; Michael J. Martin; Henry E. Johnston; Drew L. Siler

Subsurface characterization and machine learning predictions at Brady Hot Springs

By: Koenraad F. Beckers, Dmitry Duplyakin, Michael J. Martin, Henry E. Johnston, and Drew L. Siler

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Abstract

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 under-exploited 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.

Suggested Citation

Koenraad F. Beckers, Duplyakin, D., Martin, M., Johnston, H.E., Siler, D.L., 2021, Subsurface characterization and machine learning predictions at Brady Hot Springs, in Proceedings 46th Workshop on Geothermal Reservoir Engineering, v. 46, Stanford, California, February 15-17, 2021, 8 p.

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Additional publication details
Publication type	Conference Paper
Publication Subtype	Conference Paper
Title	Subsurface characterization and machine learning predictions at Brady Hot Springs
Volume	46
Year Published	2021
Language	English
Publisher	Stanford Geothermal Program
Contributing office(s)	Geology, Minerals, Energy, and Geophysics Science Center
Description	8 p.
Larger Work Type	Book
Larger Work Subtype	Conference publication
Larger Work Title	Proceedings 46th Workshop on Geothermal Reservoir Engineering
Conference Location	Stanford, California
Conference Date	February 15-17, 2021
Country	United States
State	Nevada
Other Geospatial	Brady Hot Springs