Multi-stream encoder and multi-layer comparative learning network for fluid classification based on logging data via wavelet threshold denoising

In recent years, the importance of fluid classification in oil and gas exploration has become increasingly evident. However, the inherent complexity of logging data and noise pose significant challenges to this task. To this end, this paper proposes a wavelet threshold denoising-based multi-stream encoder combined with multi-level comparison learning (LogMEC-MCL) framework for fluid classification. The framework begins with comprehensive noise reduction, utilizing wavelet threshold denoising to preprocess the data. It then extracts global temporal features by incorporating attention gated recurrent units within the multi-stream encoder. In parallel, multi-scale convolutional neural networks capture local spatial information, ensuring a more complete understanding of the data. To further improve the discriminative power of the extracted features, the framework includes two contrastive learning modules: instance-level contrastive learning and temporal contrastive learning. These components work together to refine feature differentiation, particularly in challenging cases. Additionally, the framework introduces a custom-designed loss function that combines cross-entropy loss with contrastive loss, thereby optimizing the classification performance. The proposed model was rigorously evaluated using a real-world logging dataset from the Tarim Basin in China. The experimental results demonstrate that LogMEC-MCL consistently outperforms current state-of-the-art models on two test datasets, achieving maximum classification accuracies of 95.70% and 95.50%, respectively.