Intelligent Sedimentary Lithofacies Identification With Integrated Well Logging Features

Abstract

Lithology identification is the research basis in oil and gas reservoir exploration and is critical for the formation characterization and reservoir development. Traditional lithofacies identification methods rely on the knowledge and experience of geologists and are usually done manually. With the development of deep learning technology and its application in the field of geophysics, lithofacies identification based on deep-learning approach has attracted great attention in recent years. Well logging data have obvious sequence characteristics. Therefore, we propose to use a bidirectional long and short-term memory (BiLSTM) neural network to learn long-term information for more effective lithology facies classification. In addition, we also perform correlation analysis on the input well logging curves and conduct median filter at different scales according to the correlation degree to extract the geological features within data itself and discard the interference of noise. The raw data-based lithofacies identification can reflect the noise resistance of the neural network model to some extent, while the filtered data are more beneficial for the model to extract the geological features correlated with lithofacies and provide accurate classification results. We validate our proposed framework by applying it to a case study from the Council Grove gas reservoir located in Kansas. Furthermore, we compare the effect of input data and network model on the identification results. The experimental results show that the proposed lithofacies identification method has higher classification accuracy.