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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
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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
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Alternative CAES Technology Using Depleted Unconventional Gas Wells and Subsurface Thermal Energy Storage (GeoCAES)

This project assessed the technical viability of a process called GeoCAES. The process stores electrical energy by injecting natural gas into shale gas formations using a compressor, storing it, and producing it through an expander to generate electricity. This data submission inc...

Johnston, H. and Young, D. National Renewable Energy Laboratory

May 23, 2019
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Utah FORGE Project 2439: Machine Learning for Well 16A(78)-32 Stress Predictions

This report reviews the training of machine learning algorithms to laboratory triaxial ultrasonic velocity data for Utah FORGE Well 16A(78)-32. Three machine learning (ML) predictive models were developed for the prediction of vertical and two orthogonally oriented horizontal str...

Kelley, M. et al Battelle Memorial Institute

Jun 19, 2023
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Development of a Neutron Diffraction Based Experimental Capability for Investigating Hydraulic Fractures for EGS-like Conditions

Understanding the relationship between stress state, strain state and fracture initiation and propagation is critical to the improvement of fracture simulation capability if it is to be used as a tool for guiding hydraulic fracturing operations. The development of fracture predict...

Polsky, Y. et al Oak Ridge National Laboratory

Feb 01, 2013
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Mt. Simon Sandstone Brine Chemistry for DDU Technology at the U of IL Campus

A review of brine chemistry data for the Mt. Simon Sandstone in the Illinois Basin is provided for calculations to predict the potential for mineral scaling and precipitation. The assessment includes expected changes in temperature, pressure, and/or exposure to air or other materi...

Lu, Y. and McKaskle, R. University of Illinois

Mar 31, 2019
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Oregon Cascades Play Fairway Analysis: Maps

The maps in this submission include: heat flow, alkalinity, Cl, Mg, SiO2, Quaternary volcanic rocks, faults, and land ownership. All of the Oregon Cascade region. The work was done by John Trimble, in 2015, at Oregon State University.

Trimble, J. University of Utah

Dec 15, 2015
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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
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Appalachian Basin Play Fairway Analysis Thermal Risk Factor and Quality Analyses

*This submission revises the analysis and products for Thermal Quality Analysis for the northern half of the Appalachian Basin (https://gdr.openei.org/submissions/638)* This submission is one of five major parts of a Low Temperature Geothermal Play Fairway Analysis. Phase 1 of the...

Jordan, T. Cornell University

Aug 02, 2016
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CO2 Push-Pull Dual (Conjugate) Faults Injection Simulations

This submission contains datasets and a final manuscript associated with a project simulating carbon dioxide push-pull into a conjugate fault system modeled after Dixie Valley- sensitivity analysis of significant parameters and uncertainty prediction by data-worth analysis. Datas...

Oldenburg, C. et al Lawrence Berkeley National Laboratory

Jul 20, 2017
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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
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Deep Direct-Use Feasibility Study Numerical Modeling and Uncertainty Analysis using iTOUGH2 for West Virginia University

To reduce the geothermal exploration risk, a feasibility study is performed for a deep direct-use system proposed at the West Virginia University (WVU) Morgantown campus. This study applies numerical simulations to investigate reservoir impedance and thermal production. Because of...

Garapati, N. et al West Virginia University

Dec 20, 2019
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