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

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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

United States Geological Survey. (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]. Retrieved from https://dx.doi.org/10.15121/1832133.
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Siler, Drew, Pepin, Jeff D., Vesselinov, Velimir V., Mudunuru, Maruti K., and Ahmmed, Bulbul. 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: N.p., 01 Oct, 2021. Web. doi: 10.15121/1832133.
Siler, Drew, Pepin, Jeff D., Vesselinov, Velimir V., Mudunuru, Maruti K., & Ahmmed, Bulbul. 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. https://dx.doi.org/10.15121/1832133
Siler, Drew, Pepin, Jeff D., Vesselinov, Velimir V., Mudunuru, Maruti K., and Ahmmed, Bulbul. 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". United States. https://dx.doi.org/10.15121/1832133. https://gdr.openei.org/submissions/1344.
@div{oedi_1344, 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, Pepin, Jeff D., Vesselinov, Velimir V., 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).}, doi = {10.15121/1832133}, url = {https://gdr.openei.org/submissions/1344}, journal = {}, number = , volume = , place = {United States}, year = {2021}, month = {10}}
https://dx.doi.org/10.15121/1832133

Details

Data from Oct 1, 2021

Last updated Nov 23, 2021

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

DOE Project Details

Project Name Insightful Subsurface Characterizations and Predictions

Project Lead Mike Weathers

Project Number 35517

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