Deep Learning-Based Intelligent Characterization of Sedimentary Microfacies Using Logging Curve Images: Model Comparison and Application

Intelligent identification and characterization of sedimentary microfacies is the core content of oil and gas reservoir geological research, which has important guiding significance for the analysis of reservoir space distribution law and the optimization of oil and gas reservoir development scheme. Traditional interpretation methods rely on manual judgment of logging response parameters, limited by the difference in subjective experience of interpreters, and machine learning as a new method is gradually replacing some manual judgments. Since machine learning does not accurately determine all deposited sedimentary microfacies, it is necessary to adequately compare multiple models. In this study, taking the Cretaceous Yaojia Formation (K2y1) member of the Putaohua oil layer in the satellite oilfield of Songliao Basin as the research object, this study builds on existing research and proposes a deep learning sedimentary microfacies intelligent identification method based on the time series image features of logging curves, and constructs three typical models for comparison, namely CNN, ResNet and Transformer. By converting five logging curves, natural gamma (GR), natural potential (SP), acoustic time difference (AC), deep lateral resistivity (LLD) and shallow lateral resistivity (LLS), into temporal images, the morphological parameters reflecting sedimentary dynamics are extracted, and the correspondence between logging response characteristics and sedimentary microfacies is established. The research results show that CNN, ResNet, and Transformer have accuracy rates of 83%, 85%, and 90% respectively on the validation set. Through the comprehensive evaluation of multiple indicators, the applicable conditions and optimization directions of different models in the identification of sedimentary microfacies in heterogeneous reservoirs are clarified. This method system provides an improved approach for interpreting complex reservoir sedimentary microfacies, and provides a new technical approach for multi-model comparison and optimization for intelligent exploration of hidden oil and gas reservoirs.