Search OEDI Data

Utah FORGE 3-2418: Wellbore Fracture Imaging Using Inflow Detection Measurements Workshop Presentation

This is a presentation on the Wellbore Fracture Imaging Using Inflow Detection Measurements project by Stanford University, presented by Dr. Roland N. Horne. The project's objective was to make measurements in the Utah FORGE wells, both before and after fracturing experiments, to...

Horne, R. and Su, J. Stanford University

Sep 08, 2023
1 Resources
0 Stars
Publicly accessible

EGS Collab Experiment 1: DNA tracer data on transport through porous media

This submission contains DNA tracer data that supports the analysis and conclusions of the publication, "DNA tracer transport through porous media The effect of DNA length and adsorption." https://doi.org/10.1029/2020WR028382. This experiment used DNA as an artificial reservoir t...

Zhang, Y. et al Stanford University

Nov 21, 2020
3 Resources
0 Stars
Publicly accessible

EGS Collab Experiment 1: Time-series geochemistry data of the long-term circulation test

This submission presents the weekly geochemistry data of the long-term flow test performed within EGS Collab Experiment 1 from early 2019 to early 2020. The fluids from each producing borehole/interval (PI, PB, PDT and PST) along with the injectate were sampled roughly weekly from...

Zhang, Y. et al Stanford University

Jan 01, 2022
4 Resources
0 Stars
Publicly accessible

Stanford Thermal Earth Model for the Conterminous United States

Provided here are various forms of the Stanford Thermal Earth Model, as well as the data and methods used for its creation. The predictions produced by this model were visualized in two-dimensional spatial maps across the modeled depths (0-7 km) for the conterminous United States....

Aljubran, M. and Horne, R. Stanford University

Mar 14, 2024
9 Resources
0 Stars
Publicly accessible

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources

Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Di...

Ahmmed, B. Stanford University

Apr 04, 2022
3 Resources
0 Stars
Publicly accessible