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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

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

Soda Lake Geothermal: Raw 3D and 3C Seismic-Reflection Data from 2010 Survey

Sep 01, 2010
172.11 GB
Publicly accessible
This dataset contains seismic-reflection records created in 2010 around the Soda Lake geothermal field near Fallon, Nevada. The data was collected by the power plant operator at the time, Magma Energy (CYRQ Energy in 2024). This was a petroleum-industry-quality three-dimensional ...
Authors
N. Louie, J. et al University of Nevada Reno
geothermalenergysoda lake geothermalseismic dataraw data3dseismic reflectionseg-yfield logsproject reportssoda lakenevadavibroseisfallon3cshot recordsfield recordssurveyseismic surveymagmageophysics

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

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