An Intelligent Lithofacies Identification Method of PCNN-SVM Fused with Attention Mechanism and Zebra Optimization Algorithm: Preliminary Results from the Zigong Block, Sichuan Basin

Precise lithofacies identification is essential for efficient deep shale gas exploration and predictive targeting of reservoir “sweet spots.” However, deep shale reservoirs exhibit strong heterogeneity. In addition, well-log signals are inherently susceptible to environmental noise, instrumental limitations, and acquisition anomalies, which further complicate facies classification and fine-scale characterization. Traditional facies recognition methods rely heavily on expert interpretation and qualitative assessment, resulting in low automation and limited quantitative accuracy. Moreover, because of the strong nonlinearity of well-log data and the complex coupling among geological features, existing machine learning models remain limited in deep feature extraction, adaptive representation, and generalization, making them insufficient for complex subsurface conditions. To address these challenges, this study proposes a novel intelligent facies identification framework, ZOA-PCNN-AT-SVM, which integrates the Zebra Optimization Algorithm (ZOA), Parallel Convolutional Neural Networks (PCNN), and a multi-head attention mechanism. Through multidimensional collaborative optimization, the framework enables accurate deep-feature extraction and efficient facies recognition. First, Gramian Angular Fields (GAF) are used to convert one-dimensional well-log sequences into two-dimensional topological images, thereby leveraging the image-learning capability of CNNs. A parallel dual-branch convolutional architecture is then designed to enhance feature extraction through multi-branch complementarity. Subsequently, a multi-head attention mechanism is introduced to adaptively weight critical features and strengthen the capture of complex geological correlations. Finally, ZOA searches for the initial learning rate and convolutional kernel sizes within predefined bounds, selecting the configuration with the lowest observed validation-set classification error. Using data from 10 cored wells in the ZG block, the dataset was divided into training, testing, and validation sets at a ratio of 7:2:1. On the held-out test set comprising 120 samples from two wells, the ZOA-PCNN-AT-SVM model achieved an overall accuracy of 96.67% and a macro-F1 score of 96.72%. In a separate field evaluation, the model achieved an accuracy of 92.6% on Well L1. Application to an additional deep shale gas well in the LZ block achieved an accuracy of 91.7%, providing preliminary support for the model’s cross-block applicability.