With the increasing demand for precise lithology identification in oil exploration and development, traditional logging methods are no longer fully adapted to the challenges of complex geological conditions in modern oil and gas fields. Artificial intelligence technology, especially various algorithms in machine learning, has shown breakthrough progress in data analysis and image recognition of geophysical logging, effectively overcoming many limitations of traditional methods. This study extensively reviews relevant research in recent years and analyzes the current application status and development trends of artificial intelligence in key directions such as lithology identification, low resistivity oil layer identification, reservoir parameter evaluation, and fracture and pore identification in the field of logging. This article specifically addresses the challenges of identifying igneous rock lithology and quantitatively evaluating fractures. Four algorithms in machine learning, including decision trees, random forests, gradient boosting trees, and Bayes, are used to intelligently identify the lithology of oil and gas reservoirs in Block S. By analyzing the geological characteristics of volcanic rock reservoirs in the study area and the logging response characteristics of different rock types, 8 characteristic parameters, including M and N, were determined to be extremely sensitive to volcanic rock lithology. The research results indicate that the random forest algorithm performs the best in model accuracy and generalization ability, with an accuracy rate of over 86%, providing an effective intelligent method for identifying volcanic rock lithology using conventional logging curves. The high-precision recognition and prediction ability of this model provides a solid technical foundation for the exploration and development of volcanic rock reservoirs.