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

• Published in: Scientific reports Vol. 14; no. 1; p. 10339
• Main Authors: Shi, Haoxin, Zhang, Yanjun, Yu, Ziwang, Yang, Yunxing
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06.05.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 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
• DOI: 10.1038/s41598-024-59409-5

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.