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Utah FORGE: Phase 2C Seismic Observation and Ground Water Well Data and Well Locations
This submission contains the following data associated with Utah FORGE Phase 2C within the Roosevelt Hot Springs geothermal area:
An ArcGIS shapefile with well locations for wells 58-32, 78-32, 68-32, OH-3, OH-4, WOW-2, and WOW-3. Well 58-32 is the deep test well, wells 68-32 an...
Abraham, S. et al Energy and Geoscience Institute at the University of Utah
Mar 26, 2019
6 Resources
0 Stars
Curated
6 Resources
0 Stars
Curated
Utah FORGE Phase 3 Native State Model: 2022 Update
This is the Phase 3 native state model update. The Phase 3 numerical model represents a significant subsurface volume below the FORGE site footprint. The model domain of 4.0 km x 4.0 km x 4.2 km is located approximately between depths of 4000 to 4200 meters below land surface. Thi...
Podgorney, R. and Liu, R. Idaho National Laboratory
Jul 29, 2022
1 Resources
0 Stars
Curated
1 Resources
0 Stars
Curated
EGS Collab Experiment 1: Microseismic Monitoring
The U.S. Department of Energy's Enhanced Geothermal System (EGS) Collab project aims to improve our understanding of hydraulic stimulations in crystalline rock for enhanced geothermal energy production through execution of intensely monitored meso-scale experiments. The first expe...
Schoenball, M. et al Lawrence Berkeley National Laboratory
Jul 29, 2019
45 Resources
0 Stars
Curated
45 Resources
0 Stars
Curated
Analyzed Boise Data for Oscillatory Hydraulic Tomography
Data here has been "pre-processed" and "analyzed" from the raw data submitted to the GDR previously (raw data files found at http://gdr.openei.org/submissions/479. doi:10.15121/1176944 after 30 September 2017). First, we submit .mat files which are the "pre-processed" data (must h...
Lim, D. University of Wisconsin
Jul 01, 2015
8 Resources
0 Stars
Curated
8 Resources
0 Stars
Curated
Utah FORGE: Temperature Contours
This submission contains several ArcGIS shapefiles, each with Temperature contour lines at different depths. Subsurface temperature were important for characterizing the geothermal system beneath the FORGE site in Milford, Utah.
Energy and Geoscience Institute at the University of Utah
Mar 09, 2016
5 Resources
0 Stars
Publicly accessible
5 Resources
0 Stars
Publicly accessible
Utah FORGE: Well 78B-32 Core Sample Petrographic Analysis Data
This dataset contains an overview of the petrographic, X-ray Diffraction and scanning electron microscopy analyses of core samples from Utah FORGE Well 78B-32 and related data as described in the .zip folder's Description below.
Jones, C. Energy and Geoscience Institute at the University of Utah
Feb 21, 2023
1 Resources
0 Stars
Curated
1 Resources
0 Stars
Curated
Literature Data on Foam Fracturing Fluid
At the beginning of this project, the Temple team spent significant effort to collect data relevant to foam fracturing. More than 40 articles/reports were found in the open literature that reported the properties of aqueous foams under various testing conditions. The foam properti...
Thakor, V. et al Temple University
Nov 08, 2021
2 Resources
0 Stars
Curated
2 Resources
0 Stars
Curated
Utah FORGE: GIS Well Temperature Data
This is a GIS point feature shapefile representing wells, and their temperatures, that are located in the general Utah FORGE area near Milford, Utah. There are also fields that represent interpolated temperature values at depths of 200 m, 1000 m, 2000 m, 3000 m, and 4000 m. in deg...
Gwynn, M. et al Energy and Geoscience Institute at the University of Utah
Feb 28, 2018
1 Resources
0 Stars
Publicly accessible
1 Resources
0 Stars
Publicly accessible
Sentinel-1 Input Data for PSInSAR Analysis
Files used to perform the Persistent Scatterer InSAR analysis with SARPROZ. The data is sourced from ESAs Sentinel-1 project and covers Brady Hot Springs and Desert Peak geothermal areas. The original titles are included for the Sentinel-1 data. The naming guide is included as a l...
Moraga, J. Colorado School of Mines
Apr 29, 2021
73 Resources
0 Stars
Curated
73 Resources
0 Stars
Curated
Processed Lab Data for Neural Network-Based Shear Stress Level Prediction
Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a n...
Marone, C. et al Pennsylvania State University
May 14, 2021
3 Resources
0 Stars
Curated
3 Resources
0 Stars
Curated
Hybrid machine learning model to predict 3D in-situ permeability evolution
Enhanced geothermal systems (EGS) can provide a sustainable and renewable solution to the new energy transition. Its potential relies on the ability to create a reservoir and to accurately evaluate its evolving hydraulic properties to predict fluid flow and estimate ultimate therm...
Elsworth, D. and Marone, C. Pennsylvania State University
Nov 22, 2022
4 Resources
0 Stars
Curated
4 Resources
0 Stars
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Magnetotelluric Data Collected in 2016 over the San Emidio Geothermal Field in Nevada
This data set includes the magnetotelluric (MT) data collected from October 21 to November 9, 2016 over the San Emidio geothermal field in Nevada by Quantec Geoscience USA Inc. on behalf of US Geothermal Inc. as part of a project entitled "A Novel Approach to Map Permeability Usi...
Folsom, M. et al Ormat Technologies, Inc.
Nov 09, 2016
11 Resources
0 Stars
Curated
11 Resources
0 Stars
Curated
DEEPEN: Newberry Volcano MT and Gravity Data 2022 and 2023 Acquisition and Processing
As part of DEEPEN (DE-risking Exploration of geothermal Plays in magmatic ENvironments), a 3D play fairway analysis (PFA) was conducted at Newberry Volcano in Central Oregon for multiple play types (conventional hydrothermal, superhot EGS, and supercritical). For use in this PFA, ...
Shultz, A. et al Enthalpion Energy
Jun 30, 2023
8 Resources
0 Stars
Curated
8 Resources
0 Stars
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Dataset for report: Non-Technical Barriers to Geothermal Development in California and Nevada
In California and Nevada, geothermal projects are subject to non-technical barriers, which may create development delays leading to higher project costs and risks and decreased competitiveness with other electricity generation technologies. These non-technical barriers may include...
Levine, A. et al National Renewable Energy Laboratory
Mar 10, 2022
19 Resources
0 Stars
Curated
19 Resources
0 Stars
Curated