OpenEI: Energy Information
  • Geothermal Data Repository
  • My User
    • Sign Up
    • Login
 
  • Data
    • View All Submissions
    • Data Lakes
    • Data Standards
    • Submit Data
  • Help
    • Frequently Asked Questions
    • Data Submission Best Practices
    • Data Submission Tutorial Videos
    • Instructions for Funds Recipients
    • Data Provision Guidelines
    • Contact GDR Help
  • About
  • Search

Search GDR Data

Showing results 1 - 6 of 6.
Show results per page.
Order by:
Available Now:
Filters Clear All Filters ×
Technologies
Featured Projects
Topics
Data Type
"SGW"×
Stimulations×

Using Fully Coupled Hydro-Geomechanical Numerical Test Bed to Study Reservoir Stimulation with Low Hydraulic Pressure

This paper documents our effort to use a fully coupled hydro-geomechanical numerical test bed to study using low hydraulic pressure to stimulate geothermal reservoirs with existing fracture network. In this low pressure stimulation strategy, fluid pressure is lower than the minimu...
Fu, P. et al Lawrence Livermore National Laboratory
Jan 31, 2012
2 Resources
0 Stars
Publicly accessible

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs

Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulat...
Beckers, K. et al National Renewable Energy Laboratory
Feb 18, 2021
1 Resources
0 Stars
Publicly accessible

A Thermal-Hydrological-Chemical Model for the EGS Demonstration Project at Newberry Volcano, OR

Newberry Volcano in Central Oregon is the site of a Department of Energy funded Enhanced Geothermal System (EGS) Demonstration Project. Stimulation and production of an EGS is a strong perturbation to the physical and chemical environment, giving rise to coupled Thermal-Hydrologic...
Sonnenthal, E. et al National Energy Technology Laboratory
Jan 30, 2012
1 Resources
0 Stars
Publicly accessible

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells increasing or decreasing the fluid flow rates across the wells and drilling new wells at appropriate locations. Th...
Beckers, K. et al National Renewable Energy Laboratory
Oct 20, 2021
6 Resources
0 Stars
Publicly accessible

Utah FORGE: Discrete Fracture Network (DFN) Data

The FORGE team is making these fracture models available to researchers wanting a set of natural fractures in the FORGE reservoir for use in their own modeling work. They have been used to predict stimulation distances during hydraulic stimulation at the open toe section of well 1...
Finnila, A. and Podgorney, R. Golder Associates Inc.
Jun 24, 2020
66 Resources
0 Stars
Publicly accessible

Newberry EGS Demonstration: Well 55-29 Stimulation Data 2014

The Newberry Volcano EGS Demonstration in central Oregon, a 5 year project begun in 2010, tests recent technological advances designed to reduce the cost of power generated by EGS in a hot, dry well (NWG 55-29) drilled in 2008. First, the stimulation pumps used were designed to ru...
Cladhouhos, T. et al AltaRock Energy Inc
Sep 03, 2015
54 Resources
0 Stars
Publicly accessible
  • About the GDR
  • Partners & Sponsors
  • Disclaimers
  • Developer Services
  • The GDR is the submission point for all data collected from research funded by the U.S. Department of Energy's Geothermal Technologies Office.
  • Content is available under Creative Commons Attribution 4.0 unless otherwise noted.

Privacy Policy Notification

This site uses cookies to store and share user preferences with other OpenEI sites, and uses Google Analytics to collect anonymous user information such as which pages are visited, for how often, and what searches or other webpages may have led users here. You can prevent Google Analytics from recognizing you on return visits to this site by disabling cookies on your browser or by installing a Google Analytics Opt-out Browser Add-on. By clicking "Accept" you agree this site can store cookies on your device and disclose information to OpenEI and Google Analytics in accordance with our privacy policy.

OpenEI Privacy Policy Google Analytics Terms of Service