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GOOML Kahunanui Data Curation, Historical Modeling, Forecast Modeling, and Genetic Optimization Examples

Jan 30, 2023
725.34 MB
Awaiting release
This dataset contains example files and Jupyter Notebooks associated with the Geothermal Operational Optimization using Machine Learning (GOOML) framework, specifically for the fictional Kahunanui (KHN) geothermal power plant. The dataset includes synthetic time series data, confi...
Authors
Taverna, N. et al Upflow
geothermalenergymachine learninggoomlpower plantoptimizationgenetic optimizationregressionneural networkoperationssynthetic datakahunanuiforecasthindcastdata curationinputsoutputsconfigurationexamplephygnnphysics guided neural networkssteamfieldsteam fieldwellsflash plantsprocessed datapythonjupyter notebookmodelmodelingcode

Utah FORGE: 2020-2021 Geothermal Energy/EGS Survey and Results

Jul 20, 2021
4.96 MB
Publicly accessible
The Utah FORGE project collaborated with the University of Utahs Department of Communication on a Capstone course during Fall Semester 2020. The course created a survey to gauge the general populations knowledge of geothermal energy and Enhanced Geothermal Systems (EGS). The surve...
Authors
Yeo, S. and McKasy, M. Energy and Geoscience Institute at the University of Utah
geothermalenergyegsgeothermal surveyegs surveyutah forgegeothermal knowledgeegs knowledgesurveyutahqualtricsraw datadatasurvey resultsconsent formquestionnairetraining data

Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

May 14, 2021
6.01 MB
Publicly accessible
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...
Authors
Marone, C. et al Pennsylvania State University
geothermalenergymatlabgeophysicsseismiccodeprocessed datamicroseismicityaiartificial intelligencedeep learningmachine learningacousticsacoustic emissionsseismic forcastingseismic predictionfaultfault propertiesshear stresstime to failureexperimentexperimental datalab databiaxial shear experimentbiaxial shear apparatusfriction

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

Nov 22, 2022
5.58 MB
Publicly accessible
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...
Authors
Elsworth, D. and Marone, C. Pennsylvania State University
geothermalenergyegsnewberryhydraulicstimulationprocessed datamachine learningpermeability evolutionhydraulic fracturinginduced seismicityegs collabseismic data analysiswellhead pressureflow ratefracture permeabilitymicroearthquakeenhanced geothermal systems

EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography

Apr 20, 2020
6.74 MB
Publicly accessible
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...
Authors
Chai, C. et al Oak Ridge National Laboratory
geothermalenergyegs collab3d seismic structuretransfer learningdeep learningmachine learninginteractivesurfp-waves-wavemicroseismic catalogseismic tomographyinteractive visualizationgeophysicsmodelingvelocitymodelmicroseismicitycatalogtransfer-learningdouble-difference tomography3dseismicmeqprocessed datageospatial data

Utah FORGE: Phase-Picking Based Microseismic Event Catalog April 2024 Stimulation

Feb 19, 2026
1.73 MB
Publicly accessible
This catalog contains microseismic event locations recorded during the April 2024 stimulation at the Utah FORGE site. Events were detected and located using a phase-picking workflow that integrates downhole geophones (wells 56 and 78B) and downhole DAS (well 16B). P and S-wave arr...
Authors
Zhu, W. et al University Of Utah
geothermalenergyapril 2024 stimulationforgeutahmicroseismic event locationsutah forgeuniversity of utahseismicaimachine learningdata-driven decisionscommunity safteyinfrastructure protectionproactive risk mitigationimproved safteyimmediate responsestimulationfracingground motionseismic hazardsegsevent catalogmlprocessed datareservoir characterizationhydraulic fracturinggeomechanicsgeophysics

Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments

Feb 07, 2023
Size unavailable
Publicly accessible
Previous moderate and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable mode...
Authors
Mordensky, S. et al United States Geological Survey
geothermalenergypfahydrothermalenergy assessmentresource assessmentretrospectivemachine learninggeosciencewestern usdata-drivenbias reductionfavorabilitymappingegslow tempprocessed dataresourcecharacterization

Sentinel-1 Input Data for PSInSAR Analysis

Apr 29, 2021
305.43 GB
Publicly accessible
Files used to perform the Persistent Scatterer InSAR analysis with SARPROZ. The data is sourced from ESAs Sentinel-1 project and covers Brady Hot Springs and Desert Peak geothermal areas. The original titles are included for the Sentinel-1 data. The naming guide is included as a l...
Authors
Moraga, J. Colorado School of Mines
geothermalenergyinsarpsinsarsentinel-1synthetic aperture radarsatellite imagingaiartificial intelligencedeep learningmachine learningaerial photographyremote sensingsite detectionanomaly detectioninput datageologydisplacementsubsoilphenomenaprocessed dataradarimaginginterferometricanalysissoil deformationgeospatial data
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