3-D Geologic Controls of Hydrothermal Fluid Flow at Brady Geothermal Field, Nevada using PCA


In many hydrothermal systems, fracture permeability along faults provides pathways for groundwater to transport heat from depth. Faulting generates a range of deformation styles that cross-cut heterogeneous geology, resulting in complex patterns of permeability, porosity, and hydraulic conductivity. Vertical connectivity (a through going network of permeable areas that allows advection of heat from depth to the shallow subsurface) is rare and is confined to relatively small volumes that have highly variable spatial distribution. This local compartmentalization of connectivity represents a significant challenge to understanding hydrothermal circulation and for exploring, developing, and managing hydrothermal resources. Here, we present an evaluation of the geologic characteristics that control this compartmentalization in hydrothermal systems through 3-D analysis of the Brady geothermal field in western Nevada. A published 3-D geologic map of the Brady area is used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity. The 3-D distribution of these variables is compared to the distribution of productive and non-productive fluid flow intervals along production wells and non-productive wells via principal component analysis (PCA). This comparison elucidates which geologic and structural variables are most closely associated with productive fluid flow intervals. Results indicate that production intervals at Brady are located: (1) within or near to known and stress-loaded macro-scale faults, and (2) in areas of high fault and fracture density.

This submission includes the published journal article detailing this work, the published 3-D 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, 3-D well data, along which geologic data were sampled for PCA analyses, and associated metadata file. This work was done using existing R programs.

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DOE Project Name: Insightful Subsurface Characterizations and Predictions
DOE Project Number: 35517
DOE Project Lead: Mike Weathers
DOI: 10.15121/1832132
Last Updated: 7 months ago
Data from October, 2021
Submitted Nov 10, 2021


United States Geological Survey


Publicly accessible License 


Drew Siler
United States Geological Survey
Jeff D. Pepin
United States Geological Survey


geothermal, energy, PCA, 3D geologic model, geologic model, geology, characterization, machine learning, ML, BHS, Brady Hot Springs, principal component analysis, production, stress, faults, R, Brady, hydrothermal, geologic structure, unsupervised, 3D well data, code, geothermic, geophysics


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