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Utah FORGE Project 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
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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
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Utah FORGE: Fault Shear Reactivation Experimental Data for Fluid Injection-Rate Controls on Seismic Moment

Included are experimental data recorded from shear experiments that specifically explore the link between fluid-injection rate and seismic moment resulting from shear reactivation of laboratory faults. Raw mechanical data from three experiments are included alongside corresponding...

Roseboom, M. et al Pennsylvania State University

Nov 07, 2023
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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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Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets

This dataset contains turbine and plant-level power outputs for 252,500 cases of diverse wind plant layouts operating under a wide range of yawing and atmospheric conditions. The power outputs were computed using the Gaussian wake model in NREL's FLOw Redirection and Induction in ...

Ramos, D. et al National Renewable Energy Laboratory

Feb 12, 2021
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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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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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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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Utah FORGE Friction-Permeability-Seismicity Laboratory Experiments with Non-Linear Acoustics

Laboratory experimental data on saw-cut interface of Westerly Granite and Utah Forge granitoid rocks. Experiments include velocity-stepping and fluid pressure stepping experiments. Mechanical data from 3 ISCO pumps connected to a Temco pressure vessel measure axial, confining and ...

Eijsink, A. and Elsworth, D. Pennsylvania State University

Jul 08, 2022
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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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Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites

The 'Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties' project looks to apply machine learning (ML) methods to Microearthquake (MEQ) data for imaging geothermal reservoir properties and forecasting seismic events, in order to...

Zhu, T. Pennsylvania State University

May 05, 2021
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Airfoil Computational Fluid Dynamics 9k shapes, 2 AoA's

This dataset contains aerodynamic quantities including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) for 8,996 airfoil shapes, computed using the HAM2D CFD (computational fluid dynamics) model. The airfoil shapes ...

Ramos, D. et al National Renewable Energy Laboratory (NREL)

Feb 10, 2023
3 Resources
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Airfoil Computational Fluid Dynamics 2k shapes, 25 AoA's, 3 Re numbers

This dataset contains aerodynamic quantities including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) for 1,830 airfoil shapes computed using the HAM2D CFD (computational fluid dynamics) model. The airfoil shapes w...

Ramos, D. et al National Renewable Energy Laboratory (NREL)

Feb 10, 2023
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Brady's Geothermal Field Nodal Seismometer Earthquake Data

90-second records of data from 238 three-component nodal seismometer deployed at Bradys geothermal field. The time window catches an earthquake arrival. Earthquake data from USGS online catalog: Magnitude: 4.3 ml +/ 0.4 Location: 38.479 deg N 118.366 deg W +/ 0.7 km Depth: 9.9 km...

Feigl, K. University of Wisconsin

Mar 21, 2016
3 Resources
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Super-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI)

Super-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI) introduces machine learning methods to incorporate high-resolution Urban Heat Island (UHI) effects into low-resolution historical reanalysis and future climate model datasets. The dataset includes model...

Buster, G. et al The National Renewable Energy Lab (NREL)

Oct 16, 2024
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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
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3-D Geologic Controls of Hydrothermal Fluid Flow at Brady Geothermal Field, Nevada using PCA

In many hydrothermal systems, fracture permeability along faults provides pathways for groundwater to transport heat from depth. Faulting generates a range of deformation styles that cross-cut heterogeneous geology, resulting in complex patterns of permeability, porosity, and hydr...

Siler, D. and Pepin, J. United States Geological Survey

Oct 01, 2021
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Utah FORGE: Fault Reactivation Through Fluid Injection Induced Seismicity Laboratory Experiments

Included are results from shear reactivation experiments on laboratory faults pre-loaded close to failure and reactivated by the injection of fluid into the fault. The sample comprises a single-inclined-fracture (SIF) transecting a cylindrical sample of Westerly granite. All expe...

Yu, J. et al Penn State University

Jul 01, 2023
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