Resilient Semi-Supervised Meta-Learning Network based on wavelet transform and K-means optimization for fluid classification

In the field of geological exploration, accurately distinguishing between different types of fluids is crucial for the development of oil, gas, and mineral resources. Due to the scarcity of labeled samples, traditional supervised learning methods face significant limitations when processing well log data. To address this issue, this paper presents a novel fluid classification method known as the Resilient Semi-Supervised Meta-Learning Network (RSSMLN) based on wavelet transform and K-means optimization, which combines the advantages of few-shot learning and semi-supervised learning, aiming to optimize fluid recognition in well log data. Initially, this study employs a small set of labeled samples to train the initial model and utilizes pseudo-label generation and K-means clustering to optimize prototypes, thereby enhancing the model's accuracy and generalization ability. Subsequently, during the feature extraction process, wavelet transform preprocessing techniques are introduced to enhance the time-frequency feature representation of well log data through multi-scale decomposition. This process effectively captures high-frequency and low-frequency features, providing structured information for subsequent convolution operations. By employing a dual-channel heterogeneous convolutional kernel feature extractor, RSSMLN can effectively capture subtle features of the fluids and significantly improve classification accuracy. Experimental results indicate that compared to various standard deep learning models, RSSMLN achieves superior performance in fluid identification tasks. This research provides a reliable solution for few-shot fluid recognition in oilfield applications and offers scientific support for resource exploration and evaluation.