Bayesian-Optimized XGBoost for High-Precision Lithofacies Identification in Carbonate Reservoirs Using Geophysical Well Logs

Carbonate reservoirs possess substantial hydrocarbon resource potential and have become a major focus of exploration in recent years. However, the complex geological settings, pronounced lithological heterogeneity, and strong vertical variability of carbonate reservoirs pose significant challenges to conventional lithofacies identification methods. Such approaches are often time-consuming, highly subjective, and limited in their ability to accurately discriminate complex lithofacies assemblages, thereby constraining the efficient development of carbonate reservoirs. Geophysical well logging offers advantages such as low acquisition cost, continuous coverage, and high vertical resolution, making it a fundamental dataset for continuous lithofacies characterization. In this study, integrated geological information, including whole-rock analysis, petrographic thin sections, and scanning electron microscopy, is employed to systematically investigate the mineral compositions and pore structure characteristics of different lithofacies, and the corresponding logging response mechanisms are quantitatively investigated. Five conventional logging curves—gamma ray, acoustic, neutron porosity, bulk density, and resistivity—are selected to construct a multidimensional feature parameter set. Considering the differences in numerical ranges among logging curves and the imbalance in lithofacies sample distributions, data normalization and class imbalance correction are performed prior to model training. Subsequently, a high-precision lithofacies identification model based on conventional well logs is developed by integrating Bayesian optimization with the Extreme Gradient Boosting (XGBoost) algorithm. The results indicate that the predicted lithofacies are highly consistent with geological interpretations, core observations, and thin-section identifications, demonstrating that the Bayesian-optimized XGBoost model exhibits robust classification performance and effectively captures the complex nonlinear relationships between lithofacies and logging responses. Compared with traditional machine learning methods, the proposed approach achieves significantly improved identification accuracy, providing robust technical support and a reliable theoretical foundation for carbonate reservoir evaluation and hydrocarbon exploration.