Deep learning-based approach for reservoir fluid identification in low-porosity, low-permeability reservoirs

The complex pore structure and weak logging responses in low-porosity, low-permeability reservoirs pose challenges for traditional methods to identify reservoir fluids. Additionally, the imbalance in fluid categories leads to classification bias toward majority classes, reducing recognition performance for minority categories. To address these challenges, this paper proposes a Reservoir Fluid Identification method that combines an improved synthetic minority over-sampling technique (SMOTE) with a revisiting mobile convolutional neural network (CNN) from vision transformer (ViT) perspective (RepViT)-transformer hybrid model. Logging data is processed using a sliding window to generate sequence samples that preserve stratigraphic information while optimizing sample structure for CNN. The improved SMOTE algorithm employs a symmetric generation strategy to expand Minority Class Samples while maintaining data distribution characteristics, mitigating the class imbalance problem. The hybrid model integrates the local feature extraction of RepViT and the global dependency capturing capabilities of transformer, enhancing Classification Accuracy and Stability. Applied to logging data from the Dagang Oilfield, the method outperformed bidirectional long short-term memory (BiLSTM) and transformer models in both overall recognition performance and minority class identification, achieving 94.41% accuracy on the test set—4.92% and 10.24% higher than BiLSTM and transformer, respectively. These results demonstrate the method's effectiveness in identifying fluids in low-porosity, low-permeability reservoirs, offering a novel approach for fluid evaluation using conventional logging data.