四川盆地川西坳陷上三叠统须家河组是四川盆地致密砂岩气（以下简称致密气）增储上产的重要领域。在实际生产中，高产稳产井与裂缝密集发育高度相关，裂缝为气体的运移和保存提供了路径和场所，裂缝发育与否成为制约优质储层形成的关键因素。为了评价须家河组气藏富集“甜点”区，依据岩心观察、测井资料及智能化算法，明确裂缝发育特征并建立有效的裂缝识别方法。研究认为：研究区的构造裂缝、成岩裂缝与异常高压裂缝均有发育。其中，构造裂缝主要分为3期，第1期NW—SE（北西—南东）向主要发育低角度裂缝，偶尔可见高角度裂缝；第2期NNE—SSW（北北东—南南西）向主要发育高角度裂缝；第3期E—W（东—西）向主要发育高角度裂缝。致密气储层裂缝层段具有低密度、高补偿中子、高声波时差、冲洗带电阻率和地层电阻率呈现正幅度差。对带有裂缝和非裂缝标签的常规测井数据进行归一化处理，应用机器学习算法进行裂缝智能化预测，K近邻算法、支持向量机、极端梯度提升树算法和随机森林算法的F₁分数分别为0.65、0.83、0.88、0.91，发现随机森林算法具有较强的鲁棒性和抗干扰能力，预测精确度和效率均高于其他3种算法。同时，为了兼顾运算效率与准确性，选择基因遗传算法作为优化算法进行超参数调优，优于网格搜索、贝叶斯优化及粒子群优化算法。使用沙普利可加性特征解释方法（SHapley Additive Explanations， 简称SHAP）计算不同影响因素对预测的贡献值，发现声波时差、补偿中子和补偿密度为主要影响预测效果的测井曲线。裂缝密度呈现出明显的空间分布规律，即从四川盆地西南部至四川盆地西北部，裂缝密度依次降低。研究结果可为四川盆地西部地区致密气储层裂缝“甜点”区预测提供一套切实可行的智能化预测模型，为致密气增储上产奠定基础。

The Upper Triassic Xujiahe Formation in the western Sichuan Depression, Sichuan Basin is an important area for the increase in reserves and production of tight sandstone gas (hereinafter referred to as “tight gas”) in the Sichuan Basin. In practical production, high-yield and stable production wells are highly correlated with the dense development of fractures. Fractures provide pathways and spaces for gas migration and storage, and whether fractures develop is a key factor restricting the formation of high-quality reservoirs. To evaluate the “sweet spot” enrichment area of the Xujiahe Formation gas reservoir, fracture development characteristics are identified and an effective fracture recognition method is established based on core observation, logging data, and intelligent algorithms. The research suggests that structural fractures, diagenetic fractures, and overpressure fractures all develop in the study area. The structural fractures are mainly divided into three phases: Phase 1 (NW-SE orientation) predominantly develops low-angle fractures, with occasional high-angle fractures; Phase 2 (NNE-SSW orientation) mainly develops high-angle fractures; Phase 3 (E-W orientation) predominantly develops high-angle fractures. The fracture segments in the tight gas reservoir display characteristics of low density, high neutron density, high sonic time difference, and positive amplitude differences in deep and shallow lateral resistivity. The conventional logging data with fracture and non-fracture labels were normalized, and machine learning algorithms were applied for fracture intelligent prediction. The F1 scores for the K-nearest neighbors (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), and random forest algorithms were 0.65, 0.83, 0.88, and 0.91, respectively. It was found that the random forest algorithm demonstrated strong robustness and anti-interference capabilities, with higher prediction accuracy and efficiency compared to the other three algorithms. Additionally, to balance computational efficiency and accuracy, the genetic algorithm was selected as the optimization algorithm for hyperparameter tuning, outperforming grid search, Bayesian optimization, and particle swarm optimization algorithms. Shapley Additive Explanations (SHAP) were used to calculate the contribution of different influencing factors to the predictions. It was found that the sonic time difference, neutron density, and compensated density were the main logging curves influencing prediction accuracy. The fracture density exhibited a clear spatial distribution pattern, decreasing from the southwestern part to the northwestern part of the Sichuan Basin. The research results can provide a practical and feasible intelligent prediction model for the fracture “sweet spot” zone in tight gas reservoirs in the western Sichuan Basin, laying the foundation for increasing reserves and production of tight gas.