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Hybrid machine learning model to predict 3D in-situ permeability evolution

Enhanced geothermal systems (EGS) can provide a sustainable and renewable solution to the new energy transition. Its potential relies on the ability to create a reservoir and to accurately evaluate its evolving hydraulic properties to predict fluid flow and estimate ultimate therm...

Elsworth, D. and Marone, C. Pennsylvania State University

Nov 22, 2022
4 Resources
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EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography

This package contains a 3D Seismic velocity model and an updated microseismic catalog associated with a proceedings paper (Chai et al., 2020) published in the 45th Workshop on Geothermal Reservoir Engineering. The 3D_seismic_velocity_model text file contains x (m), y(m), z(m), P-w...

Chai, C. et al Oak Ridge National Laboratory

Apr 20, 2020
7 Resources
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Utah FORGE 3-2535: Preliminary Report on Development of a Reservoir Seismic Velocity Model

This report describes the development of a preliminary 3D seismic velocity model at the Utah FORGE site and first results from estimating seismic resolution in the generated fracture volume during Stage 3 of the April 2022 stimulation. A preliminary 3D velocity model for the larg...

Gritto, R. Array Information Technology

Jan 30, 2023
1 Resources
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Appendices for Geothermal Exploration Artificial Intelligence Report

The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especia...

Duzgun, H. et al Colorado School of Mines

Jan 08, 2021
12 Resources
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Utility-Scale Solar, 2023 Edition: Analysis of Empirical Plant-level Data from U.S. Ground-mounted PV, PV+battery, and CSP Plants (exceeding 5 MWAC)

Berkeley Labs "Utility-Scale Solar", 2023 Edition presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developm...

Seel, J. et al Lawrence Berkeley National Lab

Sep 05, 2023
5 Resources
1 Stars
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M3 Wave DMP/APEX WEC Numerical Survivability Report Baseline Geometry

Summary of numerical survivability modeling method for the baseline geometry of the Delos-Reyes Morrow Pressure Device (DMP), commercialized by M3 Wave LLC as "APEX."

Roberts, J. et al M3 Wave

Aug 16, 2016
2 Resources
0 Stars
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Utah FORGE 5-2615: Shear Enhanced Permeability In a Granitoid Fracture Presentation Slides

Provided here is a set of presentation slides detailing stress-dependent permeability in FORGE granitoid fractures and how fracture slip affects permeability. It outlines empirical correlations between permeability, stress, and fracture aperture, emphasizing that mechanically clos...

Ghassemi, A. and Ye, Z. University of Oklahoma

Feb 28, 2024
1 Resources
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Utility-Scale Solar, 2024 Edition: Analysis of Empirical Plant-level Data from U.S. Ground-mounted PV, PV+battery, and CSP Plants (exceeding 5 MWAC)

Berkeley Labs "Utility-Scale Solar", 2024 Edition presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developm...

Seel, J. et al Lawrence Berkeley National Lab

Sep 27, 2024
5 Resources
1 Stars
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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
0 Stars
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Utah FORGE 2-2439v2: Report on Predicting Far-Field Stresses Using Finite Element Modeling and Near-Wellbore Machine Learning for Well 16A(78)-32

This report presents the far-field stress predictions at two locations along the vertical section of Utah FORGE Well 16A (78)-32 using a physics-based thermo-poro-mechanical model. Three principal stresses in far-field were obtained by solving an inverse problem based on the near-...

Lu, G. et al University of Pittsburgh

Aug 30, 2024
2 Resources
0 Stars
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Utah FORGE 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
1 Resources
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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
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GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files

This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework ...

Buster, G. et al Upflow

Jun 30, 2021
11 Resources
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Geomechanical Modeling for Thermal Spallation Drilling

Wells for Engineered Geothermal Systems (EGS) typically occur in conditions presenting significant challenges for conventional rotary and percussive drilling technologies: granitic rocks that reduce drilling speeds and cause substantial equipment wear. Thermal spallation drilling,...

Walsh, S. et al Lawrence Livermore National Laboratory

Aug 24, 2011
1 Resources
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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
1 Resources
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Control-based optimization for tethered tidal kite

This submission includes three peer-reviewed (under review) papers from the researchers at North Carolina State University presenting control-based techniques to maximize effectiveness of a tethered tidal kite. Below are the abstracts of each file included in the submission. Cobb...

Vermillion, C. et al North Carolina State University

Mar 02, 2020
3 Resources
0 Stars
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Utah FORGE: Optimization of a Plug-and-Perf Stimulation (Fervo Energy)

Information around the plug-and-perf treatment design at Utah FORGE by Fervo Energy. Objective and Purpose: Develop a multistage hydraulic stimulation approach designed specifically to target the top three factors that control the technical and commercial viability of an EGS sys...

Norbeck, J. et al Fervo Energy

Feb 08, 2023
3 Resources
0 Stars
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Deep Direct-Use Feasibility Study Computed Tomography (CT)-scanned data Analysis for the Tuscarora Sandstone at the National Energy Technology Laboratory

The computed tomography (CT) facilities at the National Energy Technology Laboratory (NETL) Morgantown, West Virginia site were used to characterize core of the Tuscarora Sandstone from a vertical well in Preston County WV, the Preston-119 from a depth of 7,165 to 7,438 ft. The pr...

Brown, S. et al West Virginia University

Jan 10, 2020
11 Resources
0 Stars
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EGS Collab Experiment 1: SIMFIP Notch-164 GRL Paper

Characterizing the stimulation mode of a fracture is critical to assess the hydraulic efficiency and the seismic risk related to deep fluid manipulations. We have monitored the three-dimensional displacements of a fluid-driven fracture during water injections in a borehole at ~1.5...

Guglielmi, Y. Lawrence Berkeley National Laboratory

Sep 24, 2020
9 Resources
0 Stars
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Coso Geothermal Spectral Library for Rocks and Minerals

An integrated open mineral spectral library designed to enhance the utility and precision of mineral spectral data for geothermal exploration, developed from a reliable and comprehensive digital dataset for seamless sharing by integrating field data, the USGS spectral library, and...

Cavur, M. et al Mining Engineering Department of Colorado School of Mines

Aug 23, 2023
5 Resources
0 Stars
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Graph Theory for Analyzing Pair-wise Data: Application to Interferometric Synthetic Aperture Radar Data

Graph theory is useful for estimating time-dependent model parameters via weighted least-squares using interferometric synthetic aperture radar (InSAR) data. Plotting acquisition dates (epochs) as vertices and pair-wise interferometric combinations as edges defines an incidence gr...

Reinisch, E. University of Wisconsin

Jul 28, 2016
1 Resources
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Phased Retrofits in Existing Homes in Florida Phase I: Shallow and Deep Retrofits

The U.S. Department of Energy's Building America research team Building America Partnership for Improved Residential Construction is collaborating with Florida Power & Light (FPL) to conduct a phased residential energy-efficiency retrofit program. This research seeks to establish ...

Beal, D. et al University of Central Florida Florida Solar Energy Center

Apr 27, 2016
10 Resources
0 Stars
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OPFLearnData: Dataset for Learning AC Optimal Power Flow

The datasets are resulting from OPFLearn.jl, a Julia package for creating AC OPF datasets. The package was developed to provide researchers with a standardized way to efficiently create AC OPF datasets that are representative of more of the AC OPF feasible load space compared to t...

Joswig-Jones, . et al National Renewable Energy Laboratory

Oct 26, 2021
12 Resources
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Measure Guideline: Deep Energy Enclosure Retrofit for Interior Insulation of Masonry Walls and Flat Roofs

Measure Guideline: Deep Energy Enclosure Retrofit for Zero Energy Ready House Flat Roofs BSC TO5 Task 7.4 Deep Energy Enclosure Retrofit (DEER) for Interior Insulation of Masonry Walls Lawrence, MA 01840 This Measure Guideline describes a deep energy enclosure retrofit solution ...

Musunuru, S. et al Building Science Corporation

Apr 27, 2016
5 Resources
0 Stars
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Utah FORGE: Deep Well 58-32 (MU-ESW1) Core Data

These datasets, images, and graphics were derived from core drilling and core that was extracted from Utah Forge deep well 58-32 (originally called MU-ESW1), near Roosevelt Hot Springs.

Nash, G. and Moore, J. Energy and Geoscience Institute at the University of Utah

Apr 11, 2018
5 Resources
0 Stars
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