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Appendices for Geothermal Exploration Artificial Intelligence Report

Jan 08, 2021
2.76 GB
Publicly accessible
The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especia...
Authors
Duzgun, H. et al Colorado School of Mines
geothermalenergyartificial intelligencehydrothermally altered mineralsmineral markerssvmgeodatabasewellfaultseismicaiborderbradydesert peaksalton sealand surface temperaturedeformationgeophysicalgeophysicssupport vector machinehyperspectralhyperspectral imagingcalifornianevadaegsblindblind systemdeep learningmachine learningexplorationgeospatial datashort wavelength infraredswirdatabaseanomaly detectionsite detectionradarhydrothermalmodelconceptual modelzoteroraw datapreproccessedprocessed dataenhanced geothermal systemengineered geothermal systemremote sensingarcgisgisinsarmorphologymorphologicalmorphological featurestirvnirvisible near infraredthermal infraredcodepython

Supercritical Drilling Materials Analysis: Report on Resources, Challenges, and Needed Technologies

May 01, 2026
19.98 MB
Awaiting release
This report describes a comprehensive review of 49 existing supercritical geothermal wells to inform future geothermal drilling attempts and avoid previous failure points and understand material and testing limitations, while collating information gathered in a publicly accessible...
Authors
Kibikas, W. et al Sandia National Laboratories
geothermalenergysupercriticaldrilling materialswell completionwellborematerial validationmaterial testinglaboratory experimentswell casingcasing connectionscementdrill bitfluid chemistrylost circulation materialslinerdrill stringsmaterial performancefailure pointsliterature reviewsupercritical wellsgeothermal wellsgeochemistrycirculationdrilling fluidstechnical reportnesjavellirhengillreykjaneskraflalarderellocampi flegreinisyroskakkondapunathe geyserssalton sealos humerosmenengai

Simbol Materials Lithium Extraction Operating Data From Elmore and Featherstone Geothermal Plants

Jul 01, 2015
253.5 kB
Publicly accessible
The data provided in this upload is summary data from its Demonstration Plant operation at the geothermal power production plants in the Imperial Valley. The data provided is averaged data for the Elmore Plant and the Featherstone Plant. See average brine composition tab for submi...
Authors
Harrison, S. Simbol, Inc
geothermalsalton seaelmorefeatherstonelithiummineral extractionicp-oesbrine treatmentfetherstonesimbol materialsextractionimperial valleybrinebrine compositiondemonstrationchemistrywater chemistrygeochemistry