Search GDR Data

Showing results 1 - 3 of 3.
Show results per page.
Order by:
Available Now:
Clear All Filters ×
Topic
Technologies
Demonstration Sites
Data Type
"Utah Geological Survey maps"×
Reservoir Thermal Energy Storage (RTES)×

Dataset and SUTRA model used to evaluate Reservoirs for Thermal Energy Storage in the Portland Basin, Oregon.

Jun 29, 2020
Size unavailable
Publicly accessible
This is a link to the open access, published dataset and modeling that supports a feasibility study of Reservoir Thermal Energy Storage (RTES) in the Portland Basin, Oregon, USA. Citation: Burns, E.R., 2020, SUTRA model used to evaluate Saline or Brackish Aquifers as Reservoirs f...
Authors
Bershaw, J. et al Portland State University
geothermalenergymodelrtesportland basinoregonsutratessimulationviscositydensityflowstimulationhydraulicaquifersalinebrackishporositywell-spacingwell datadistrict heatingheatingcoolingthermal efficiencyheat fluxtemperaturevolumepumping rate

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

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