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GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files

Jun 30, 2021
13.6 MB
Publicly accessible
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 ...
Authors
Buster, G. et al Upflow
geothermalenergymachine learningoptimizationoperationssynthetic datapower plantbig kahunagoomlgenetic optimizationforecastinputsoutputsconfigurationexamplephygnnphysics guided neural networkssteamfieldsteam fieldwellsflash plantsneural networkdataprocessed datacodepythonsimulationmodel

Geologic Framework of Thermal Springs, Black Canyon, Nevada and Arizona

Aug 13, 2014
Size unavailable
Publicly accessible
This report presents the geologic framework critical in understanding spring discharge and the hydrogeology in Black Canyon directly south of Lake Mead below Hoover Dam, Nevada and Arizona. Most of the springs are thermal 2 Geologic Framework of Thermal Springs, Black Canyon, Neva...
Authors
Beard, L. et al United States Geological Survey
geothermalthermal springsblack canyonnevadaarizonahydrogeologygeologymapgeologic mappinggeochronologystructuralstructurecross sectioncross-sectionusgsfaultsfracture zonesrock characteristicshydrogeologicstratigraphy

GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico

Mar 29, 2021
179.87 MB
Publicly accessible
Our GeoThermalCloud framework is designed to process geothermal datasets using a novel toolbox for unsupervised and physics-informed machine learning called SmartTensors. More information about GeoThermalCloud can be found at the GeoThermalCloud GitHub Repository. More information...
Authors
Vesselinov, V. Los Alamos National Laboratory
geothermalenergymachine-learningnew mexicobradynevadagreat basinsouthwest new mexicomulti-physicsbrady hot springssmarttensorsgeothermalcloudgeothermal cloudlos alamos national laboratorysite datasimulationmachine learningmodel

Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files

Apr 15, 2022
113.79 MB
Publicly accessible
This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the synthesized data for reservoir thermal energy storage site identification. In this study, a machine-learning-assiste...
Authors
Jin, W. et al Idaho National Laboratory
reservoir thermal energy storagestochastic simulationgeotesmachine learningmodelingtesht-rtescharacterizationnumerical modelstochastichydrogeologic formationsimulated datasimulation datahigh-temperaturethermal energy storageoptimizationartificial neural network regressionannneural networkoperation scenariosseasonal-cyclepareto frontsseasonal operationcontinuous operationfalconmoose

District Geothermal Energy Systems with Seasonal Underground Thermal Storage: Load Profiles, Modeling Workflow, and Techno-Economic Results for U.S. Screening

Sep 27, 2025
135.99 MB
Publicly accessible
This dataset accompanies the paper "Geothermal district energy systems coupled with seasonal underground thermal energy storage: a U.S. techno-economic screening by climate and geology." It contains the data and scripts required to reproduce the study's results across ten U.S. cit...
Authors
Mello, S. et al National Laboratory of the Rockies
geothermalenergydistrict energy systemsunderground thermal energy storageutesatesrtesground heat exchangerghesutratecho-economic analysisenergy modelingcomstockbuilding load profileslcoeaquifer storageseasonal storageu.s. citiespythonmodelscriptsmodel datasubsurface simulationgeotescold utes

Hourly Thermal Load Profiles for U.S. Manufacturing Subsectors

Apr 24, 2026
3.92 MB
Publicly accessible
This dataset includes a consistent framework of annual, quarterly, and hourly energy demand and operational profiles for 63 U.S. manufacturing subsectors defined by NAICS classification, building on the county-level demand dataset "Updated U.S. Low-Temperature Heating and Cooling ...
Authors
Oh, H. et al National Laboratory of the Rockies
geothermalenergythermal energy demandindustrial energy demandmanufacturingu.s. manufacturingnaicseia mecsqpchourly load profilesoperating schedulescapacity utilizationprocess heatingprocess coolingrefrigerationboiler usefacility heatingfacility coolingend-use energyfuel typeprocessed data

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

Oct 01, 2021
5.68 MB
Publicly accessible
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...
Authors
Siler, D. et al United States Geological Survey
geothermalenergynmfkbrady hot springsmachine learningmlbhsnonnegative matrix factorization k-meanshydrothermalbradyk-meansclusteringnonnegative matrix factorizationmatrix factorizationgeothermalcloudsmarttensorsunsupervised3d well data3d geologic mapgeologic structurefaultsstressgeologycharacterizationgeologic modelproductioncode

Exploration for Blind Geothermal Resources in the State of Hawaii Utilizing Dissolved Noble Gasses in Well Waters

Nov 01, 2020
139.07 kB
Publicly accessible
This study is an extension of the Hawaii Play Fairway Analysis (PFA), a statewide geothermal exploration project funded by the United States Department of Energy. Based on results from prior phases of the PFA, this project targeted 66 wells on the islands of Hawaii, Maui, Lanai, O...
Authors
Ferguson, C. University of Hawaii
geothermalenergyhawaiiplay fairwaykilaueamauilanaikauainoble gasheliumtrace metalr/raisotopeindicatorindicator gasresource detection

Procurement Options for Low Temperature Geothermal Technologies at Federal Facilities

Sep 30, 2023
Size unavailable
Publicly accessible
Included here are access links to a report on procurement options for low temperature geothermal technologies at federal facilities from Pacific Northwest National Laboratory. Federal agencies are moving towards more efficient and resilient facilities by increasingly implementing...
Authors
Heiland, M. et al Pacific Northwest National Laboratory
geothermalenergyfederal facilitiesprocurementgeothermal heat pumpsbuildingslow temperature geothermalghptechnical reportheating and coolingdistrict heatingenergy saving performance contractsespchvac

Utah FORGE: Neubrex RFS DSS Fiber Optic Strain Data from Well 16B(78)-32 August 2025 Circulation and Huff-and-Puff Tests

Mar 26, 2026
10.33 GB
Publicly accessible
This dataset contains processed Neubrex Rayleigh Frequency Shift Distributed Strain Sensing data acquired in well 16B(78)-32 at the Utah FORGE site during August 2025 field operations. The data were collected during cross-well circulation testing from well 16A to well 16B and subs...
Authors
Guzik, A. et al Neubrex Energy Services (US), LLC
geothermalenergyfiber opticsstraindtsdastemperatureacousticshuffpuffcirculationcoiled tubingsingle modemulti modegaugeutah forgeegsneubrexrayleigh frequency shiftrfsdistributed strain sensingdssfiber optic sensingprocessed dataprodmlhdf5strain changestrain change rate16b78-32circulation testhuff and puffgeophysics

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

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

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Oct 20, 2021
6.41 MB
Publicly accessible
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...
Authors
Beckers, K. et al National Renewable Energy Laboratory
geothermalenergymachine learningmlsubsurfacecharacterizationbrady hot springsbhspredictionreservoir modelingtime seriespcaprincipal component analysisreservoir managementreservoirdual-porositystimulationinjection testpdetemperatureflowpressuresimulationsingle-fracturedoubletheat maptensorflowcnnlstmmlphydrothermalopen source reservoirosrnevada

Appalachian Basin Temperature-Depth Maps and Structured Data in support of Feasibility Study of Direct District Heating for the Cornell Campus Utilizing Deep Geothermal Energy

Oct 29, 2019
229.82 MB
Publicly accessible
This dataset contains shapefiles and rasters that summarize the results of a stochastic analysis of temperatures at depth in the Appalachian Basin states of New York, Pennsylvania, and West Virginia. This analysis provides an update to the temperature-at-depth maps provided in the...
Authors
Smith, J. Cornell University
geothermalenergycornell universitylow-temperature geothermalreservoir simulationuncertainty analysisthermal datadistrict heatingdirect-use heatingdduappalachian basinnew york statetechno-economic analysislevelized cost of heat lcohexternality valuesenvironmental valuemonte carlo analysismonte carloeconomiceaheat pumpghpshapefilerastercornellgeospatial data
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