"Womp Womp! Your browser does not support canvas :'("

Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk

Publicly accessible License 

In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fuid-fow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fuid-fow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate: (1) the specific geologic controls on the Brady hydrothermal system and (2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes.

This submission includes the published journal article detailing this work, the published 3D geologic map of the Brady Geothermal Area used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity, 3D well data, along which geologic data were sampled for PCA analyses, and associated metadata file. This work was done using the GeoThermalCloud framework, which is part of SmartTensors (both are linked below).

Citation Formats

TY - DATA AB - In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fuid-fow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fuid-fow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate: (1) the specific geologic controls on the Brady hydrothermal system and (2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes. This submission includes the published journal article detailing this work, the published 3D geologic map of the Brady Geothermal Area used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity, 3D well data, along which geologic data were sampled for PCA analyses, and associated metadata file. This work was done using the GeoThermalCloud framework, which is part of SmartTensors (both are linked below). AU - Siler, Drew A2 - Pepin, Jeff D. A3 - Vesselinov, Velimir V. A4 - Mudunuru, Maruti K. A5 - Ahmmed, Bulbul DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Renewable Energy Laboratory DO - 10.15121/1832133 KW - geothermal KW - energy KW - NMFK KW - Brady Hot Springs KW - machine learning KW - ML KW - BHS KW - Nonnegative Matrix Factorization k-means KW - hydrothermal KW - Brady KW - k-means KW - clustering KW - nonnegative matrix factorization KW - matrix factorization KW - GeoThermalCloud KW - SmartTensors KW - unsupervised KW - 3D well data KW - 3D geologic map KW - geologic structure KW - faults KW - stress KW - geology KW - characterization KW - geologic model KW - production KW - code LA - English DA - 2021/10/01 PY - 2021 PB - United States Geological Survey T1 - Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk UR - https://doi.org/10.15121/1832133 ER -
Export Citation to RIS
Siler, Drew, et al. Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk. United States Geological Survey, 1 October, 2021, GDR. https://doi.org/10.15121/1832133.
Siler, D., Pepin, J., Vesselinov, V., Mudunuru, M., & Ahmmed, B. (2021). Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk. [Data set]. GDR. United States Geological Survey. https://doi.org/10.15121/1832133
Siler, Drew, Jeff D. Pepin, Velimir V. Vesselinov, Maruti K. Mudunuru, and Bulbul Ahmmed. Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk. United States Geological Survey, October, 1, 2021. Distributed by GDR. https://doi.org/10.15121/1832133
@misc{OEDI_Dataset_7460, title = {Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk}, author = {Siler, Drew and Pepin, Jeff D. and Vesselinov, Velimir V. and Mudunuru, Maruti K. and Ahmmed, Bulbul}, abstractNote = {In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fuid-fow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fuid-fow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate: (1) the specific geologic controls on the Brady hydrothermal system and (2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes.

This submission includes the published journal article detailing this work, the published 3D geologic map of the Brady Geothermal Area used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity, 3D well data, along which geologic data were sampled for PCA analyses, and associated metadata file. This work was done using the GeoThermalCloud framework, which is part of SmartTensors (both are linked below).}, url = {https://gdr.openei.org/submissions/1344}, year = {2021}, howpublished = {GDR, United States Geological Survey, https://doi.org/10.15121/1832133}, note = {Accessed: 2025-05-09}, doi = {10.15121/1832133} }
https://dx.doi.org/10.15121/1832133

Details

Data from Oct 1, 2021

Last updated May 21, 2024

Submitted Nov 10, 2021

Organization

United States Geological Survey

Contact

Drew Siler

Authors

Drew Siler

United States Geological Survey

Jeff D. Pepin

United States Geological Survey

Velimir V. Vesselinov

Los Alamos National Laboratory

Maruti K. Mudunuru

Pacific Northwest National Laboratory

Bulbul Ahmmed

Los Alamos National Laboratory

Research Areas

DOE Project Details

Project Name Insightful Subsurface Characterizations and Predictions

Project Lead Mike Weathers

Project Number 35517

Share

Submission Downloads