Reservoir temperature prediction based on characterization of water chemistry data—case study of western Anatolia, Turkey

Reservoir temperature estimation is crucial for geothermal studies, but traditional methods are complex and uncertain. To address this, we collected 83 sets of water chemistry and reservoir temperature data and applied four machine learning algorithms. These models considered various input factors a...

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Veröffentlicht in:	Scientific reports Jg. 14; H. 1; S. 10339 - 15
Hauptverfasser:	Shi, Haoxin, Zhang, Yanjun, Yu, Ziwang, Yang, Yunxing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 06.05.2024 Nature Publishing Group Nature Portfolio
Schlagworte:	639/4077/909/4083 704/172 704/2151 Accuracy Algorithms Bayesian analysis Boron Carbon Case studies Chemistry Geothermal power Geothermal resources Geothermal studies Geothermometer Humanities and Social Sciences Hydrogeochemistry Learning algorithms Machine learning Mathematical models Methods multidisciplinary Neural networks Predictions Regression analysis Reservoir temperature Reservoirs Science Science (multidisciplinary) Temperature Water chemistry Turkey Hydrogeochemistry Reservoir temperature Geothermometer Machine learning
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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Zusammenfassung:	Reservoir temperature estimation is crucial for geothermal studies, but traditional methods are complex and uncertain. To address this, we collected 83 sets of water chemistry and reservoir temperature data and applied four machine learning algorithms. These models considered various input factors and underwent data preprocessing steps like null value imputation, normalization, and Pearson coefficient calculation. Cross-validation addressed data volume issues, and performance metrics were used for model evaluation. The results revealed that our machine learning models outperformed traditional fluid geothermometers. All machine learning models surpassed traditional methods. The XGBoost model, based on the F-3 combination, demonstrated the best prediction accuracy with an R 2 of 0.9732, while the Bayesian ridge regression model using the F-4 combination had the lowest performance with an R 2 of 0.8302. This study highlights the potential of machine learning for accurate reservoir temperature prediction, offering geothermal professionals a reliable tool for model selection and advancing our understanding of geothermal resources.
Bibliographie:	ObjectType-Case Study-2 SourceType-Scholarly Journals-1 content type line 14 ObjectType-Feature-4 ObjectType-Report-1 ObjectType-Article-3 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-024-59409-5