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

Siler, Drew; Pepin, Jeff D.; Vesselinov, Velimir V.; Mudunuru, Maruti K.; Ahmmed, Bulbul

doi:10.15121/1832133

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

Abstract

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. Thismore »« less

Authors:

; Pepin, Jeff D.; Vesselinov, Velimir V.; Mudunuru, Maruti K.; Ahmmed, Bulbul

Publication Date:: Fri Oct 01 00:00:00 EDT 2021

Other Number(s):: 1344

DOE Contract Number:: 35517

Research Org.:: USDOE Geothermal Data Repository (United States); United States Geological Survey

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)

Collaborations:: United States Geological Survey

Subject:: 15 Geothermal Energy

Keywords:: geothermal; energy; NMFK; Brady Hot Springs; machine learning; ML; BHS; Nonnegative Matrix Factorization k-means; hydrothermal; Brady; k-means; clustering; nonnegative matrix factorization; matrix factorization; GeoThermalCloud; SmartTensors; unsupervised; 3D well data; 3D geologic map; geologic structure; faults; stress; geology; characterization; geologic model; production; code

Geolocation:: 83.0,180.0|-83.0,180.0|-83.0,-180.0|83.0,-180.0|83.0,180.0

OSTI Identifier:: 1832133

DOI:: https://doi.org/10.15121/1832133

Project Location:

Citation Formats


                    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., 2021. 
        Web.  doi:10.15121/1832133.

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                    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.  doi:https://doi.org/10.15121/1832133

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                    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.  doi:https://doi.org/10.15121/1832133.  https://www.osti.gov/servlets/purl/1832133. Pub date:Fri Oct 01 00:00:00 EDT 2021

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@article{osti_1832133,

  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).},

  doi          = {10.15121/1832133},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Fri Oct 01 00:00:00 EDT 2021},

  month        = {Fri Oct 01 00:00:00 EDT 2021}

}

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DOI: https://doi.org/10.15121/1832133

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Works referenced in this record:

Three-dimensional geologic map of the Brady geothermal area, Nevada
report, January 2021

Siler, Drew L.; Faulds, James E.; Hinz, Nicholas H.
https://doi.org/10.3133/sim3469

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Similar Records

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

Abstract

Citation Formats

Three-dimensional geologic map of the Brady geothermal area, Nevada report, January 2021

Three-dimensional geologic map of the Brady geothermal area, Nevada
report, January 2021