Prediction of Reservoir Temperatures Using Hydrogeochemical Data, Western Anatolia Geothermal Systems (Turkey): A Machine Learning Approach

Basit Öğe Kaydı
dc.authoridHAKLIDIR, FUSUN SERVIN TUT/0000-0002-9469-8870|HAKLIDIR, MEHMET/0000-0003-4985-1116
dc.authorwosidHAKLIDIR, FUSUN SERVIN TUT/ABI-3978-2020
dc.authorwosidHAKLIDIR, MEHMET/ABI-3996-2020
dc.contributor.authorHaklidir, Fusun S. Tut
dc.contributor.authorHaklidir, Mehmet
dc.date.accessioned2024-07-18T20:42:15Z
dc.date.available2024-07-18T20:42:15Z
dc.date.issued2020
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractGeothermal fluids can be used for purposes such as power production, district heating/cooling, agriculture, and industrial and thermal tourism. Although using geothermal fluids is beneficial, it requires detailed exploration studies of a region. These exploration studies mainly involve geology, geophysics and geochemistry disciplines to understand the location, dimensions, possible capacity and temperature of a reservoir before beginning drilling operations. Because of the high operational costs, the exploration phase of a geothermal project is of great importance to reduce project costs. Evaluation of existing earth sciences data, detailed geology studies, mapping and some geochemical studies, such as using geothermometers, can provide information about a potential geothermal reservoir in a geothermal field. Machine learning is a technology for data analysis which identifies patterns in data and uses them to make predictions about new data points automatically. In this study, a deep learning model is developed to predict geothermal reservoir temperatures based on selected hydrogeochemistry data from different geothermal systems. Two traditional regression approaches, linear regression and linear support vector machine, are performed to compare the prediction performance of our proposed deep learning model. The objective of the study is to obtain the algorithm having the lowest root-mean-square error and mean absolute error. The results show that the deep neural network (DNN) algorithm generated the lowest errors. The DNN model provided the most accurate values close to geothermometer calculations for reservoir temperature. The performance comparison showed that our deep learning model achieved the best prediction performance compared to traditional machine learning techniques.en_US
dc.identifier.doi10.1007/s11053-019-09596-0
dc.identifier.endpage2346en_US
dc.identifier.issn1520-7439
dc.identifier.issn1573-8981
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85076216589en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2333en_US
dc.identifier.urihttps://doi.org/10.1007/s11053-019-09596-0
dc.identifier.urihttps://hdl.handle.net/11411/7204
dc.identifier.volume29en_US
dc.identifier.wosWOS:000544723000005en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofNatural Resources Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine Learningen_US
dc.subjectDeep Learningen_US
dc.subjectHydrogeochemistryen_US
dc.subjectReservoir Temperature Predictionen_US
dc.subjectGeothermal Explorationen_US
dc.subjectWatersen_US
dc.subjectScaleen_US
dc.subjectBrineen_US
dc.titlePrediction of Reservoir Temperatures Using Hydrogeochemical Data, Western Anatolia Geothermal Systems (Turkey): A Machine Learning Approachen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

Web of Science Indexed Publications
Scopus Indexed Publications