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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
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Utah FORGE: Chemical-Based Tool Deployment Data for Inflow Detection at Wells 58-32 and 16B(78)-32 Summer 2024 Circulation Test

This dataset comprises field logging data collected during chemical-based tool deployments at the Utah FORGE geothermal site, specifically at monitoring well 58-32 and production well 16B(78)-32. Data acquisition occurred during the Summer 2024 circulation testing campaign, with t...

Sausan, S. et al Stanford University

Mar 31, 2025
12 Resources
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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
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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
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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
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