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Geocellular Model of Mt. Simon Sandstone for University of Illinois at Urbana-Champaign DDU feasibility study

The geocellular model of the Mt. Simon Sandstone was constructed for the University of Illinois at Urbana-Champaign DDU feasibility study. Starting with the initial area of review (18.0 km by 18.1 km [11.2 miles by 11.3 miles]) the boundaries of the model were trimmed down to 9.7 ...

Damico, J. University of Illinois

Dec 31, 2018
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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
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Structural Controls of the Tuscarora Geothermal Field, Elko County, Nevada MS Thesis

Detailed geologic mapping, structural analysis, and well data have been integrated to elucidate the stratigraphic framework and structural setting of the Tuscarora geothermal area. Tuscarora is an amagmatic geothermal system that lies in the northern part of the Basin and Range pr...

Dering, G. and Faulds, J. University of Nevada

May 31, 2013
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Structural Controls of the Neal Hot Springs Geothermal System, Eastern Oregon

Detailed geologic mapping (1:24,000 scale), structural and geochemical analyses, and integration of available geophysical and well-field data were utilized to assess the structural controls of the Neal Hot Springs geothermal field in eastern Oregon. The geothermal field lies withi...

Edwards, J. and Faulds, J. University of Nevada

May 31, 2013
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Utah FORGE: Well 16A(78)-32 Logs

This dataset contains all well logs from Utah FORGE well 16A(78)-32. This includes the mud log, Sanvean Technologies logs, and Schlumberger logs. Please see the file descriptions below for information about each log.

Gilmour, B. et al Energy and Geoscience Institute at the University of Utah

Mar 08, 2021
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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
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