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Brady's, NV×

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

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

Oct 01, 2021
7.12 MB
Publicly accessible
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...
Authors
Siler, D. and Pepin, J. United States Geological Survey
geothermalenergypca3d geologic modelgeologic modelgeologycharacterizationmachine learningmlbhsbrady hot springsprincipal component analysisproductionstressfaultsrbradyhydrothermalgeologic structureunsupervised3d well datacodegeothermicgeophysics

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

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

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

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 Hot Springs Seismic Modeling Data for Push-Pull Project

Jul 31, 2018
16.99 GB
Publicly accessible
This submission includes synthetic seismic modeling data for the Push-Pull project at Brady Hot Springs, NV. The synthetic seismic is all generated by finite-difference method regarding different fracture and rock properties.
Authors
Zhang, R. University of Louisiana
geothermalenergyseismicgeophysicsactive sourcevelocitygeophysicaldatamodelingnumericalsyntheticbrady hot springsnevadanvfracturerockgeologicpropertiespropertypush-pullfinite differencenumerical modelingvspvertical seismic profilingboreholefracturednon-fracturedmediaco2saturation