Abstract
This article provides a systematic review of the literature on the application of artificial intelligence (AI) to the lithological classification of deeply buried complex reservoirs, focusing on intelligent interpretation methods of well-logging data. Main results obtained By integrating the multi-stage tectonic evolution characteristics of the Junggar Basin with data science logic, an interdisciplinary deconstruction of welllog datasets based on nine logging parameters was performed. For the first time, from the perspectives of mathematical mechanisms and geophysical responses, the classification performance and convergence of Random Forest (RF) and XGBoost algorithms were compared in complex nonlinear geological spaces. A synthesis of existing studies shows that both models demonstrate extremely high accuracy in differentiating lithotypes of tight sandy-gravel reservoirs (specifically, the Baikouquan Formation of the Mahu Depression), with XGBoost systematically outperforming RF in minimizing residual gradients and uncovering latent patterns. Practical application the results provide a methodological foundation for automated welllog data interpretation, facilitate the transition of hydrocarbon exploration from an empirical paradigm to intelligent management, and help to reveal the "black box" mechanism of machine learning.