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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

Utah FORGE: Triggered DAS and Continuous Downhole Geophone Data from April 2024 Stimulations

Apr 01, 2024
35.23 TB
Curated
This dataset contains distributed acoustic sensing (DAS) and downhole geophone data collected during the April 2024 stimulation experiments at the Utah FORGE site. DAS data were acquired from well 16B(78)-32 by Neubrex and processed by GeoEnergie Suisse (GES), covering the period ...
Authors
Dyer, B. et al Energy and Geoscience Institute at the University of Utah
geothermalenergyutah forgeegsdasdistributed acoustic sensingseismictriggered eventsgeophonesdownholedownhole seismicneubrexgeoenergie suissegesaprill 2024stimulationhydraulic fracturingcountinuous dataraw dataprocessed datageophysics58-3216b78-3256-32monitoring well

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

Utah FORGE: Triggered DAS Data from the April 2024 Mini-Circulation Test

May 01, 2026
Size unavailable
In progress
This dataset contains distributed acoustic sensing (DAS) data collected during the April 2024 mini-circulation test at the Utah FORGE site. Data were acquired from wells 16B(78)-32 and 58-32 during the mini-circulation period from April 23-28, 2024, following stimulation of the in...
Authors
Dyer, B. et al Energy and Geoscience Institute at the University of Utah
geothermalenergyutah forgeegsdistributed acoustic sensingdasmini-circulation testcirculation testsegyseg-y16b78-3258-32seismic datawell trajectoriesgeophysics

Imperial Valley Dark Fiber Project Continuous DAS Data

Nov 10, 2020
1.09 TB
Publicly accessible
The Imperial Valley Dark Fiber Project acquired Distributed Acoustic Sensing (DAS) seismic data on a ~28 km segment of dark fiber between the cities of Calipatria and Imperial in the Imperial Valley, Southern California. Dark fiber refers to unused optical fiber cables in telecomm...
Authors
Ajo-Franklin, J. et al Lawrence Berkeley National Laboratory
geothermalenergydistributed acoustic sensingdasimperial valleybrawleyhidden geothermal resourcesearthquakesseismicitytectonicssouthern californiaseismic datastrain rateseismic noisedark fiberdark fiber dassalton searaw datageothermal explorationtelecommunications fiberpythonhdf5jupyter notebookgeophysicsfiber optich5py

PoroTomo Natural Laboratory Horizontal and Vertical Distributed Acoustic Sensing Data

Mar 29, 2016
342.13 TB
Publicly accessible
This dataset includes links to the PoroTomo DAS data in both SEG-Y and hdf5 (via h5py and HSDS with h5pyd) formats with tutorial notebooks for use. Data are hosted on Amazon Web Services (AWS) Simple Storage Service (S3) through the Open Energy Data Initiative (OEDI). Also include...
Authors
Feigl, K. et al University of Wisconsin
geothermalporotomodasfiber opticsurface sensorsseismic arraydistributed acoustic sensingporoeleastic tomographybradys geothermal fieldgeosciencedistributed sensingdownholetrenchedseismicityhydrothermalgeophysicsoediraw datajupyter notebookpythonhdf5hsdsh5pyh5pyd