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Brady's, NV×

Brady Geodatabase for Geothermal Exploration Artificial Intelligence

Apr 27, 2021
179.02 GB
Publicly accessible
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...
Authors
Moraga, J. et al Colorado School of Mines
geothermalenergygeodatabasebrady hot springsbradyartificial intelligenceaibrady wellseismicremote sensinghyperspectralgeospatial databasedeep learningmachine learningexplorationsite detectiongeothermal site detectionanomaly detectionshort wavelength infraredswirsupport vector machinesvmland surface temperaturelstwellraw dataprocessed datanevadaarcgismodeldatabasehydrothermalgeophysicsradargisblindblind systemdeformationgeophysicalhyperspectral imagingconceptual modelfaultpreprocessedrastervectorfield datageospatial data

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs

Feb 18, 2021
4.49 MB
Publicly accessible
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...
Authors
Beckers, K. et al National Renewable Energy Laboratory
geothermalenergymachine learningsubsurfacecharacterizationbrady hot springspredictionreservoir modelingtime seriespcaprincipal component analysisreservoir managementbradys hot springsporotomoreservoirdual-porositystimulationinjection testmodeltemperatureflowpressuresimulationsingle-fracturedoubletheatmapheat maptensorflow

Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak

Sep 01, 2020
109.94 MB
Publicly accessible
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...
Authors
Moraga, J. et al Colorado School of Mines
geothermalenergygeothermal explorationhydrothermal mineral alterationsland surface temperaturefault densitypsinsarsubsidenceupliftbrady hot springsdesert peaknevadaconvolutional neural networkfallonmachine learningmodelhydrothermalmineraltemperaturerastergeospatial datageotifftraining datatraining dataset

Understanding Constraints on Geothermal Sustainability Through Reservoir Characterization at Brady Geothermal Field, Nevada

Jul 11, 2018
4.46 MB
Publicly accessible
The vast supply of geothermal energy stored throughout the Earth and the exceedingly long time required to dissipate that energy makes the world's geothermal energy supply nearly limitless. As such, this resource holds the potential to provide a large supply of the world's energy ...
Authors
Patterson, J. University of Wisconsin
geothermalenergyhydrogeologydtsreservoir characterizationnevadabrady hot springsborehole pressureborehole temperaturedistributed temperature sensingporotomosustainabilitysustainable usesubsurface propertiesresorceidentificationassessmentproductioncharacterizationtemperaturepressurethermalhydraulicpropertiesanalyticalmodelnumericalanalysisreservoirsensitivityflow pathsfracturesflowpathwayspreferentialheattransportparameterssimulationpower production

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

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

Brady 1D Seismic Velocity Model Ambient Noise Prelim

Oct 25, 2013
0.97 kB
Publicly accessible
Preliminary 1D seismic velocity model derived from ambient noise correlation. 28 Green's functions filtered between 4-10 Hz for Vp, Vs, and Qs were calculated. 1D model estimated for each path. The final model is a median of the individual models. Resolution is best for the top 1 ...
Authors
J., R. Lawrence Livermore National Laboratory
seismicvelocity modelbradysseismic velocitygeothermalnevada

Pressure-Temperature Simulation at Brady Hot Springs

Jul 11, 2017
2.96 MB
Publicly accessible
These files contain the output of a model calculation to simulate the pressure and temperature of fluid at Brady Hot Springs, Nevada, USA. The calculation couples the hydrologic flow (Darcy's Law) with simple thermodynamics. The epoch of validity is 24 March 2015. Coordinates are ...
Authors
Feigl, K. Temple University
geothermalenergyinsar-meqporotomoinsarmicroseismicitymeqmicroearthquakeseismicityinducedsimulationpressuretemperaturebradybrady hot springs

Material Properties for Brady Hot Springs Nevada USA from PoroTomo Project

Mar 06, 2019
242.91 MB
Publicly accessible
The PoroTomo team has completed inverse modeling of the three data sets (seismology, geodesy, and hydrology) individually, as described previously. The estimated values of the material properties are registered on a three-dimensional grid with a spacing of 25 meters between nodes....
Authors
Feigl, K. and PoroTomo Team, . University of Wisconsin
geothermalenergyporotomoseismologygeodesyhydrologynevadabrady hot springsporoelastic tomographyinversionmodeling3dmaterialpropertiesunconsolidatedfracturedshallowstructuraltrendsgeologystrikedipthermal contractionpressuresubsidencepumpinghydraulic conductivityrateseismic amplitudefaultzonepermeableconduitfluidreservoirconceptualmodelpropertydensityp-waves-waveseismicvelocityyoungs moduluspoissons ratiointerferometrytemperaturelithologystrain rate