Lithology identification serves as the foundation for reservoir evaluation and prediction. However, due to the high cost of drilling coring and the relatively low reliability of mud logging lithology identification, utilizing well logging data for identification becomes crucial. The compositional and structural variations among different lithologies result in distinct well logging response characteristics, creating an urgent need to systematically evaluate the applicability of various well logging interpretation methods for lithology. This aims to maximize the utilization of well logging data for accurate lithology identification, thereby establishing a basis for sedimentary reservoir evaluation. This paper first employs the core-calibrated logging method to analyze and summarize the well logging response characteristics of typical lithologies, including sedimentary rocks, volcanic rocks, and metamorphic rocks. It then reviews how integrated utilization of conventional well logging cross plots charts enables qualitative lithology identification, while further application of well logging mineral component calculation allows for quantitative lithology discrimination. Additionally, it summarizes how emerging logging technologies such as elemental logging and imaging logging, combined with conventional methods, enhance the accuracy of lithology interpretation. Simultaneously, incorporating AI-based methods can improve the efficiency of lithology identification. Finally, the paper discusses the significance of well logging lithology interpretation in studying the reservoir "four properties" relationships. It proposes selecting appropriate comprehensive lithological interpretation through the fusion of "multi-method, multi-paramater" approaches. This study aims to provide theoretical guidance and technical support for hydrocarbon resource assessment and exploration development.