Abstract: Constrained by the strong heterogeneity within the internal structure of the strike-slip fault, the variability of reservoir spaces, and the complex distribution of fluids, the logging responses among the three characteristic zones within the strike-slip fault—namely, the fracture zone, fractured zone, and dissolution zone—are highly complex and variable. This complexity poses challenges for the effective utilization of imaging and conventional logging data to identify these three internal characteristic zones within the strike-slip fault.An analysis of the logging response characteristics of the "three zones" within the internal structure of the strike-slip fault was conducted. Sensitive logging curves were selected to construct a feature vector space based on mean and variance. The Extreme Gradient Boosting (XGBoost) algorithm was employed to establish XGBoost regression prediction models for the dissolution zone, fractured zone, and fracture zone within the strike-slip fault. Key parameters of the XGBoost model were optimized using multi-class evaluation metrics, resulting in an improved accuracy of identifying the "three zones" within the internal structure of the strike-slip fault.Utilizing the constructed XGBoost model, the identification of the "three zones" within the strike-slip fault in the study area achieved an accuracy rate of 88.89%. This indicates that the XGBoost-based identification model for the internal characteristic zones within the strike-slip fault can effectively distinguish the fracture zone, fractured zone, and dissolution zone. It holds valuable implications for a fine-grained characterization of the internal structure of the strike-slip fault.