## 1 Introduction  
Sedimentary facies identification plays a crucial role in reservoir engineering by enabling precise evaluation of reservoir properties (porosity, permeability) through depositional environment analysis (e.g., fluvial, deltaic systems). This approach delineates the spatial distribution of high-quality reservoirs while revealing heterogeneity and connectivity patterns [1]. Particularly at the microscale, targeting optimal sedimentary microfacies is the core objective of well placement. These microfacies directly govern sand-body connectivity and reservoir properties, providing the most refined basis for \"sweet spot\" identification. Accurate delineation of favorable microfacies distributions is thus paramount to well performance, making prioritized well deployment within optimal microfacies belts a fundamental strategy for enhancing productivity, mitigating geological risks, and optimizing injection-production schemes to maximize recovery efficiency [2]. Machine learning, by automating the processing of voluminous geological data, enables rapid identification of complex depositional facies and elucidation of implicit patterns, substantially improving prediction accuracy and operational efficiency. This advancement drives the transition toward precision and cost-effective reservoir development [3; 4]. Sedimentary microfacies recognition has evolved through successive waves of methodological innovation, transitioning from manual interpretation to data-driven automation. Early approaches emphasized multi-source data integration, exemplified by [PERSON] et al. 's [5] synergistic fusion of core-calibrated data, geologic parameters, and principal component-Bayesian models to quantify depositional environments. Concurrently, artificial intelligence began permeating sedimentology: [PERSON] et al. [6] enhanced facies classification accuracy via backpropagation neural networks (BPNNs), while [PERSON] [4] pioneered unsupervised learning through self-organizing maps (SOMs) by coupling geological parameters with trend code encoding. Support vector machines (SVMs) emerged as a robust tool for resolving nonlinear microfacies patterns, complemented by fuzzy logic systems [7] and BPNN-driven parameter mapping frameworks [8; 9]. These methods collectively addressed limitations of traditional experience-dependent interpretation but remained constrained by manual feature engineering and linear assumptions. Recent advances have shifted toward deep learning and hybrid paradigms, leveraging automated feature extraction and cross-domain knowledge integration. Convolutional neural networks (CNNs) revolutionized lithofacies classification by transforming 1D log curves into 2D semantic images [10; 11], while SVM-enhanced hybrid models achieved 70% accuracy in uncored intervals [11]. Unsupervised techniques like SOM enabled latent pattern discovery through topological clustering [4; 9], and multimodal frameworks integrated mathematical models with expert knowledge [12]. The integration of 1D CNNs for temporal data modeling [13] and generative adversarial networks (GANs) for synthetic log generation further expanded methodological diversity [14]. These developments underscore a paradigm shift: modern microfacies analysis prioritizes end-to-end learning architectures that transcend manual feature selection, enabling real-time, basin-scale reservoir characterization with unprecedented precision [15]. This study focuses on the target interval (55-71 m) within the Putaohua oil layer of the Weixing area. Of the 235 wells in the study area, 131 wells were selected based on stringent criteria including data integrity, completeness, and comprehensive logging suites required for robust analysis. Among these qualified wells, two cored wells provide critical lithological and sedimentological constraints, significantly enhancing the fidelity of sedimentary microfacies identification.