页岩岩相的识别、划分在页岩油气勘探开发工作中具有重要的理论及实际意义。以苏北盆地溱潼凹陷古近系阜宁组二段页岩为研究对象，通过对典型钻井溱页1井岩心样品进行全岩/黏土X射线衍射分析，应用前人的页岩矿物组分“三端元”图解，得出该地区的页岩岩相类型。同时，利用一种基于原子搜索优化算法优化的BP（反向传播算法）神经网络方法对测井信息进行数据挖掘，以此建立黏土矿物、硅质矿物、碳酸盐岩矿物相对含量的预测模型，实现了通过自然能谱到页岩矿物含量的定量表征。最后应用该模型对溱页1井和沙垛1井阜宁组二段进行岩性预测及岩相识别，其识别结果与样品实测数据的岩相划分结果高度一致。研究为实现页岩岩相的间接识别提供了一个经济、快速、高效的方法，可通过测井曲线有效地预测页岩岩相组合体的主要矿物成分，为缺乏取心、实测样品井段的岩相识别提供依据。

The identification and classification of shale lithofacies are crucial for both theoretical understanding and practical applications in shale gas exploration and exploitation. This study focuses on the shale of the second member of the Paleogene Funing Formation in the Qintong Sag, Subei Basin, using core samples from a typical drilling well, Well-Qinye-1. The research involves whole rock/clay X-ray diffraction analysis on these core samples and employs a previously developed three-terminal diagram of shale mineral components to categorize the types present in this area. Additionally, a BP neural network method optimized by the ASO（Atom Search Optimization） algorithm was utilized to perform data mining on logging information. This process aimed to establish a prediction model for the relative content of clay minerals, siliceous minerals, and carbonate minerals, achieving quantitative characterization of shale mineral content through natural gamma ray spectrometry. Ultimately, the model was applied to predict lithology and identify lithofacies in the second member of Well-Qinye-1 and Well-Shaduo-1. The identification results closely aligned with the data measured from the samples, demonstrating high consistency. This study provides an economical, rapid, and efficient method for predicting shale lithofacies and main mineral components. It also offers a foundational approach for identifying well facies in scenarios where coring and direct testing data are unavailable.