Integrating deep learning and logging data analytics for lithofacies classification and 3D modeling of tight sandstone reservoirs

The lithofacies classification is essential for oil and gas reservoir exploration and development. The traditional method of lithofacies classification is based on “core calibration logging” and the experience of geologists. This approach has strong subjectivity, low efficiency, and high uncertainty. This uncertainty may be one of the key factors affecting the results of 3D modeling of tight sandstone reservoirs. In recent years, deep learning, which is a cutting-edge artificial intelligence technology, has attracted attention from various fields. However, the study of deep-learning techniques in the field of lithofacies classification has not been sufficient. Therefore, this paper proposes a novel hybrid deep-learning model based on the efficient data feature-extraction ability of convolutional neural networks (CNN) and the excellent ability to describe time-dependent features of long short-term memory networks (LSTM) to conduct lithological facies-classification experiments. The results of a series of experiments show that the hybrid CNN-LSTM model had an average accuracy of 87.3% and the best classification effect compared to the CNN, LSTM or the three commonly used machine learning models (Support vector machine, random forest, and gradient boosting decision tree). In addition, the borderline synthetic minority oversampling technique (BSMOTE) is introduced to address the class-imbalance issue of raw data. The results show that processed data balance can significantly improve the accuracy of lithofacies classification. Beside that, based on the fine lithofacies constraints, the sequential indicator simulation method is used to establish a three-dimensional lithofacies model, which completes the fine description of the spatial distribution of tight sandstone reservoirs in the study area. According to this comprehensive analysis, the proposed CNN-LSTM model, which eliminates class imbalance, can be effectively applied to lithofacies classification, and is expected to improve the reality of the geological model for the tight sandstone reservoirs.