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Deep learning and geochemical modelling as tools for solute geothermometry

Ystroem, Lars H. ORCID iD icon 1; Vollmer, Mark 1; Nitschke, Fabian 1; Kohl, Thomas 1
1 Institut für Angewandte Geowissenschaften (AGW), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Geothermometry is constituted one of the most important geochemical tools for reservoir exploration and development. Solute geothermometers are used to estimate the temperature in the subsurface. Therefore, the chemical composition of a discharging geothermal fluid is used to infer the temperature of the reservoir. Changes in the chemical composition because of boiling, degassing, and dilution are disturbing the equilibrium state within the fluid leading to uncertainties in the temperature estimation. Especially, the pH value, the aluminium concentration, as well as boiling and dilution are parameters prone to changes. These parameters are elaborated in the geochemical modelling process to optimise these values to fit their in-situ reservoir conditions again. This geochemical modelling method can be used for multicomponent geothermometers leading to more robust and precise temperature estimations. However, this process is time-consuming, and geochemical as well as mineralogical knowledge is beneficial. Consequently, the field of artificial intelligence offers powerful methods to solve complex issues, even considering multiple unknowns. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000157209
Veröffentlicht am 27.03.2023
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Geowissenschaften (AGW)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 07.03.2023
Sprache Englisch
Identifikator ISBN: 978-2-9601946-2-3
KITopen-ID: 1000157209
HGF-Programm 38.04.04 (POF IV, LK 01) Geoenergy
Erschienen in European Geothermal Congress 2022. Proceedings
Veranstaltung European Geothermal Congress (EGC 2022), Berlin, Deutschland, 17.10.2022 – 21.10.2022
Verlag European Geothermal Energy Council (EGEC)
Schlagwörter solute geothermometry, multicomponent geothermometer, artificial neural network geothermometer
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