Reservoir fluid identification based on multi-head attention with UMAP

Gas logging curves obtained during drilling, along with conventional well logs, typically serve as foundational data for identifying reservoir fluids. Traditional methods for identifying layers containing oil or gas often suffer from limitations such as inadequate feature extraction, underutilization of differences in data points, and limited applicability. These limitations lead to reduced accuracy in predicting reservoir fluid segmentation. To address the challenge of spatiotemporal feature extraction in logging curves, this paper presents a fluid identification method based on deep learning networks and multivariate sequence classification. The method takes into account both the sequential variations in geological strata and the relationships among different curves for fluid prediction. Our proposed method utilizes a Transformer multi-head attention mechanism and follows a series of steps. First, an improved Symbolic Aggregate approXimation (SAX) is employed to assess the response characteristics of the logging curves to the target fluid and identify sensitive curves based on information similarity. Subsequently, the semi-supervised learning method UMAP is applied to identify layers with high interpretability. Hierarchical training is then employed to mitigate the impact of noise and high-dimensional nonlinearity on identification results. Finally, the processed vector curves are input into the attention module layer, where features of each layer are extracted in an encoding-decoding manner. This method has been tested and validated using gas logging data from 12 wells in a gas field offshore China. The overall prediction accuracy can exceed 94%, and the prediction accuracy for a single gas layer is over 88%. When compared to other machine learning approaches, such as K-means, decision tree, and XGBoost, this method exhibits superior identification accuracy and generalization ability, especially for unbalanced samples.