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Showing results 26 - 34 of 34.
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Conventional Hydrothermal×

Utah FORGE: Mineral Mountains West Fault System Report

This archive contains a report on the Mineral Mountains West fault system as part of phase 2C and the evidence for the northern terminus of this structure. Geologic field mapping, reprocessing of 3D seismic reflection data, and soil gas surveys contributed to this effort.
Simmons, S. and Miller, J. Energy and Geoscience Institute at the University of Utah
Jul 01, 2019
1 Resources
0 Stars
Publicly accessible

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 hydr...
Siler, D. and Pepin, J. United States Geological Survey
Oct 01, 2021
4 Resources
0 Stars
Publicly accessible

Maps, Models and Data from Southeastern Great Basin PFA

This submission includes composite risk segment models in raster format for permeability, heat of the earth, and MT, as well as the final PFA model of geothermal exploration risk in Southwestern Utah, USA. Additionally, this submission has data regarding hydrothermally altered are...
Nash, G. Energy and Geoscience Institute at the University of Utah
Jun 30, 2017
7 Resources
0 Stars
Publicly accessible

Performance, Cost, and Financial Parameters of Geothermal District Heating Systems for Market Penetration Modeling under Various Scenarios

The U.S. Department of Energy Geothermal Vision (GeoVision) Study is currently looking at the potential to increase geothermal deployment in the U.S. and to understand the impact of this increased deployment. This paper reviews 31 performance, cost, and financial parameters as inp...
Beckers, K. and Young, K. National Renewable Energy Laboratory
Mar 23, 2017
1 Resources
1 Stars
Publicly accessible

DEEPEN 3D PFA Index Models for Exploration Datasets at Newberry Volcano

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), index models needed to be de...
Taverna, N. et al National Renewable Energy Laboratory
Jun 30, 2023
6 Resources
0 Stars
Publicly accessible

Brady Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-proces...
Moraga, J. et al Colorado School of Mines
Apr 27, 2021
3 Resources
0 Stars
Publicly accessible

Utah FORGE: Seismic and Other Shapefiles from the Roosevelt Hot Springs Area

Three shapefiles in this submission show the position of proposed seismic line surveys. The mid-crustal velocity anomaly file shows the extent of an anomalously low P-wave velocity zone in the subsurface. Two other files show the extent of known hydrothermal systems in the Rooseve...
Moore, J. Energy and Geoscience Institute at the University of Utah
Mar 22, 2016
7 Resources
0 Stars
Publicly accessible

Renewable Energy Potential Model: Geothermal Supply Curves

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmenta...
Trainor-Guitton, W. et al National Renewable Energy Laboratory
Aug 21, 2023
3 Resources
1 Stars
Publicly accessible

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

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 producti...
Siler, D. et al United States Geological Survey
Oct 01, 2021
6 Resources
0 Stars
Publicly accessible
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