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 - 7 of 7.
Show results per page.
Order by:
Available Now:
Filters Clear All Filters ×
Technologies
Featured Projects
Topics
Data Type
"experimental"×
Modeling×

Numerical Modeling for Hydraulic Fracture Prediction

Numerical modeling on fused silica cylindrical materials for predicting overpressures required to fracture an homogeneous pure (surrogate) material with known mechanical properties similar to igneous rock materials and later compare these values to experimental overpressures obtai...
Gupta, V. Pacific Northwest National Laboratory
Apr 26, 2016
1 Resources
0 Stars
Publicly accessible

SMP and Fracture Modeling

The problem of loss circulation in geothermal wells is inherently challenging due to high temperatures, brittle rocks, and presence of abundant fractures. Because of the inherent challenges in geothermal environments, there are limitations in selecting proper lost circulation mate...
Salehi, S. et al University of Oklahoma
Oct 01, 2021
4 Resources
0 Stars
Publicly accessible

Chemical Impact of Elevated CO2 on Geothermal Energy Production

Numerical simulations have shown that the use of supercritical CO2 instead of water as a heat transfer fluid yields significantly greater heat extraction rates for geothermal energy. If this technology is implemented successfully, it could increase geothermal energy production and...
Carroll, S. et al Lawrence Livermore National Laboratory
Jan 01, 2013
3 Resources
0 Stars
Publicly accessible

Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions September 2023 Report

This task completion report documents the development and implementation of machine learning (ML) models for the prediction of in-situ vertical (Sv), minimum horizontal (SHmin) and maximum horizontal (SHmax) stresses in well 16A(78)-32. The detailed description of the experimental...
Mustafa, A. et al Battelle Memorial Institute
Sep 28, 2023
3 Resources
0 Stars
Publicly accessible

EGS Collab: Modeling and Simulation Working Group Teleconference Series (1-98)

This submission contains the presentation slides and recordings from the first 98 EGS Collab Modeling and Simulation Working Group teleconferences. These teleconferences served three objectives for the project: 1) share simulation results, 2) communicate field activities and resul...
White, M. et al Pacific Northwest National Laboratory
Feb 04, 2020
100 Resources
0 Stars
Publicly accessible

EGS Collab Experiment 1: Earth Model Input Files

The EGS Collab is conducting experiments in hydraulic fracturing at a depth of 1.5 km in the Sanford Underground Research Facility (SURF) on the 4850 Level. A total of eight ~60m-long subhorizontal boreholes were drilled at that depth on the western rib of the West Access Drift. S...
Neupane, G. and Sigma-V, T. Idaho National Laboratory
Dec 19, 2019
6 Resources
0 Stars
Publicly accessible

SMP Preparation, Programming, and Characterization

The problem of loss circulation in geothermal wells is inherently challenging due to high temperatures, brittle rocks, and presence of abundant fractures. Because of the inherent challenges in geothermal environments, there are limitations in selecting proper lost circulation mate...
Salehi, S. et al University of Oklahoma
Oct 01, 2021
4 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