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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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1 Resources
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Fully Coupled Geomechanics and Discrete Flow Network Modeling of Hydraulic Fracturing for Geothermal Applications
The primary objective of our current research is to develop a computational test bed for evaluating borehole techniques to enhance fluid flow and heat transfer in enhanced geothermal systems (EGS). Simulating processes resulting in hydraulic fracturing and/or the remobilization of...
Fu, P. et al Lawrence Livermore National Laboratory
Jan 01, 2011
2 Resources
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2 Resources
0 Stars
Publicly accessible
Coupling Subsurface and Above-Surface Models for Optimizing the Design of Borefields and District Heating and Cooling Systems
Accurate dynamic energy simulation is important for the design and sizing of district heating and cooling systems with geothermal heat exchange for seasonal energy storage. Current modeling approaches in building and district energy simulation tools typically consider heat conduct...
Hu, J. et al Lawrence Berkeley National Laboratory
Jan 31, 2022
10 Resources
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10 Resources
0 Stars
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Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk
In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity producti...
Siler, D. et al United States Geological Survey
Oct 01, 2021
6 Resources
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6 Resources
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Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak
The submission includes the labeled datasets, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model:
brady_som_output.gri, brady_som_output.grd, brady_som_output.*
desert_som_output.gri, desert_som_output.grd, desert_som_outpu...
Moraga, J. et al Colorado School of Mines
Sep 01, 2020
16 Resources
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16 Resources
0 Stars
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Structural Data for the Columbus Salt Marsh Geothermal Area GIS Data
Shapefiles and spreadsheets of structural data, including attitudes of faults and strata and slip orientations of faults.
Detailed geologic mapping of ~30 km2 was completed in the vicinity of the Columbus Marsh geothermal field to obtain critical structural data that would elucid...
University of Nevada
Dec 31, 2011
1 Resources
0 Stars
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1 Resources
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Publicly accessible
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
3 Resources
0 Stars
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3 Resources
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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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4 Resources
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Simulation Tools for Modeling Thermal Spallation Drilling on Multiple Scales
Widespread adoption of geothermal energy will require access to deeply buried resources in granitic basement rocks at high temperatures and pressures. Exploiting these resources necessitates novel methods for drilling, stimulation, and maintenance, under operating conditions that ...
Walsh, S. et al Lawrence Livermore National Laboratory
Jan 01, 2012
1 Resources
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1 Resources
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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
6 Resources
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6 Resources
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Desert Peak Geodatabase for Geothermal Exploration Artificial Intelligence
These files contain the geodatabases related to the Desert Peak Geothermal Field. It includes all input and output files used in the project. The files include data categories of raw data, pre-processed data, and analysis (post-processed data). In each of these categories there ar...
Moraga, J. et al Colorado School of Mines
Apr 27, 2021
3 Resources
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3 Resources
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Brady Geodatabase for Geothermal Exploration Artificial Intelligence
These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-proces...
Moraga, J. et al Colorado School of Mines
Apr 27, 2021
3 Resources
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3 Resources
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Salton Sea Geodatabase for Geothermal Exploration Artificial Intelligence
These files contain the geodatabases related to Salton Sea Geothermal Field. It includes all input and output files used with the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (po...
Moraga, J. et al Colorado School of Mines
Apr 27, 2021
3 Resources
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3 Resources
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Stimulation at Desert Peak Modeling with the Coupled THM Code FEHM
Numerical modeling of the 2011 shear stimulation at the Desert Peak Well 27-15 using a coupled thermal-hydrological-mechanical simulator. This submission contains the finite element heat and mass transfer (FEHM) executable code for a 64-bit PC Windows-7 machine, and the input and ...
Kelkar, S. et al Los Alamos National Laboratory
Apr 30, 2013
1 Resources
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1 Resources
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Programs and Code for Geothermal Exploration Artificial Intelligence
The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including...
Moraga, J. Colorado School of Mines
Apr 27, 2021
11 Resources
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11 Resources
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Utah FORGE 2-2439: A Multi-Component Approach to Characterizing In-Situ Stress: Laboratory, Modeling and Field Measurement Workshop Presentation
This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the U.S DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement project by Battelle [Columbus, OH], presented by Mark Kelley. The project's objective was to characterize stress in the U...
Kelley, M. and Bunger, A. Battelle Memorial Institute
Sep 08, 2023
1 Resources
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1 Resources
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EGS Collab Experiment 1: TOUGH2-CSM Simulation of Embedded Natural Fractures and Chemical Tracer Transport and Sorption
The EGS Collab SIGMA-V project is a multi-lab and university collaborative research project that is being undertaken at the Sanford Underground Research Facility (SURF) in South Dakota. The project consists of studying stimulation, fluid-flow, and heat transfer processes at a scal...
Johnston, B. et al National Renewable Energy Laboratory
Jun 07, 2019
4 Resources
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4 Resources
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GOOML Kahunanui Data Curation, Historical Modeling, Forecast Modeling, and Genetic Optimization Examples
This submission includes example files and Jupyter Notebooks associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Kahunanui (KHN) fictional geothermal power plant, which uses synthetic data to model a fictional plant. Includes data curation, histo...
Taverna, N. et al Upflow
Jan 30, 2023
10 Resources
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10 Resources
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In progress
Utah FORGE LBNL 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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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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12 Resources
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DEEPEN Global Standardized Categorical Exploration Datasets for Magmatic Plays
DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments.
As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), weights needed to be develop...
Taverna, N. et al National Renewable Energy Laboratory
Jun 30, 2023
4 Resources
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4 Resources
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Improved Microseismicity Detection During Newberry EGS Stimulations
Effective enhanced geothermal systems (EGS) require optimal fracture networks for efficient heat transfer between hot rock and fluid. Microseismic mapping is a key tool used to infer the subsurface fracture geometry. Traditional earthquake detection and location techniques are oft...
Lawrence Livermore National Laboratory
Oct 01, 2013
1 Resources
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1 Resources
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Improved Microseismicity Detection During Newberry EGS Stimulations
Effective enhanced geothermal systems (EGS) require optimal fracture networks for efficient heat transfer between hot rock and fluid. Microseismic mapping is a key tool used to infer the subsurface fracture geometry. Traditional earthquake detection and location techniques are oft...
Lawrence Livermore National Laboratory
Nov 01, 2013
1 Resources
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Publicly accessible
1 Resources
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Publicly accessible
Simulating Complex Fracture Systems in Geothermal Reservoirs Using an Explicitly Coupled Hydro-Geomechanical Model
Low permeability geothermal reservoirs can be stimulated by hydraulic fracturing to create Enhanced (or Engineered) Geothermal Systems (EGS) with higher permeability and improved heat transfer to increase heat production. In this paper, we document our effort to develop a numerica...
Carrigan, C. et al Lawrence Livermore National Laboratory
Jan 01, 2011
2 Resources
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2 Resources
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Utah FORGE 4-2541: Optimization and Validation of a Plug-and-Perf Stimulation Treatment Design Workshop Presentation
This is a presentation on the Optimization and Validation of a Plug-and-Perf Stimulation Treatment Design at Utah FORGE project by Fervo Energy, presented by Sireesh Dadi. The project's objective was to develop a multistage hydraulic stimulation approach designed specifically to t...
Dadi, S. and Norbeck, J. Fervo Energy
Sep 08, 2023
1 Resources
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1 Resources
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