Abstract:Reservoir lithology classification is the foundation of geological research. Although data-driven machine learning models can effectively identify reservoir lithology, the special nature of well logging data as sequential data makes it difficult for the model to effectively extract the spatial correlation of the data, resulting in limitations in reservoir identification. To address this issue, this study proposes a bidirectional long short-term memory extreme gradient boosting (BiLSTM-XGBoost, BiXGB) model for predicting reservoir lithology by combining bidirectional long short-term memory (BiLSTM) and extreme gradient boosting decision tree (XGBoost). By integrating BiLSTM into the traditional XGBoost, the model significantly enhances the feature extraction capability for well logging data. The BiXGB model utilizes BiLSTM to extract features from well logging data, which are then input into the XGBoost classification model for training and prediction. The BiXGB model achieves an overall prediction accuracy of 91% when applied to a reservoir lithology dataset. To further validate its accuracy and stability, the model is tested on the publicly available UCI Occupancy dataset, achieving an overall prediction accuracy of 93%. Compared to other machine learning models, the BiXGB model accurately classifies sequential data, improving the accuracy of reservoir lithology identification and meeting the practical needs of oil and gas exploration. This provides a new approach for reservoir lithology identification.