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An artificial neural network (ANN) as solute geothermometer

Ystroem, Lars ORCID iD icon; Vollmer, Mark; Nitschke, Fabian; Kohl, Thomas

Abstract (englisch):

The application of geothermometry has been used for the last six decades for geothermal reservoir temperature estimation. A steady evolution of conventional geothermometers to multicomponent tools as well as application of artificial intelligence are nowadays available.
The development of high-performing computers offers the possibility to use deep learning algorithm for reservoir temperature estimation. Serving a selection of geochemical input parameters to artificial neural networks, they can be used to predict temperatures in the subsurface. Therefore, the chemical composition of the geothermal fluids are required. Main cations and anions as well as the SiO2 concentration and the pH value serve as these input parameters. Using the data of well-studied geothermal systems, the neurons within the layers of the neural network are linked and weighted. Thus, the newly developed artificial intelligence is trained and validated. As a result, the modelled reservoir temperatures match with the in-situ temperature measurements of the analysed geothermal fields. Contrary to the usage of conventional geothermometers, the application of artificial neural networks are a useful novelty. ... mehr

Volltext §
DOI: 10.5445/IR/1000141910
Veröffentlicht am 14.01.2022
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Geowissenschaften (AGW)
Publikationstyp Poster
Publikationsdatum 24.09.2021
Sprache Englisch
Identifikator KITopen-ID: 1000141910
HGF-Programm 38.04.04 (POF IV, LK 01) Geoenergy
Veranstaltung 9th European Geothermal Workshop (EGW 2021), Karlsruhe, Deutschland, 23.09.2021 – 24.09.2021
Schlagwörter Artificial neural network (ANN), geothermometry, reservoir temperature estimation
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