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 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 R2 of 0.9732, while the Bayesian ridge regression model using the F-4 combination had the lowest performance with an R2 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.

Safety level assessment of shield tunneling in water rich sandy pebble strata with large particle size.

Shield tunneling method is widely used in urban metro construction. The construction stability is closely related to the engineering geological conditions. Sandy pebble strata have a loose structure and low cohesion, resulting in great engineering-induced stratigraphic disturbance. Meanwhile, the high water-abundance and strong permeability are extremely detrimental to construction safety. It is of great significance to evaluate the dangerousness of shield tunneling in water-rich pebble strata with large particle size. In this paper, risk assessment of engineering practice is carried through with Chengdu metro project in China as a case study. Referring to the special engineering situations and assessment workload, seven evaluation indices, including compressive strength of pebble layer, boulder volume content, permeability coefficient, groundwater depth, grouting pressure, tunneling speed and tunnel buried depth are selected to establish an evaluation system. A complete risk assessment framework is established based on the cloud model, AHP and entropy weight method. Further, the measured surface settlement is taken as the risk degree characterization to verify the results. This study can provide reference for method selection and evaluation system establishment in the risk assessment of shield tunnel construction in water-rich sandy pebble strata, and contribute to proposing safety management in similar engineering projects.