Identification of tight sandstone reservoir lithofacies based on CNN image recognition technology: A case study of Fuyu reservoir of Sanzhao Sag in Songliao Basin

Currently, lithofacies are automatically classified in petroleum reservoirs based on different algorithm models using well-logging vector datasets. However, well-logging vector datasets are challenging for old oil fields where there are only well-logging images dataset present. Nevertheless, tight sandstone reservoirs, which have been neglected, are becoming exploration targets in old oil fields. This has made it necessary to redefine lithofacies characteristics. Thus, this paper describes a novel, widely applicable method for using a convolutional neural network (CNN) to identify tight sandstone reservoir lithofacies, which can handle well-logging image datasets rather than well-logging vector datasets. We applied this method to the Sanzhao Sag in the Northern Songliao Basin of China using the following steps: (1) dividing typical lithofacies based on core and well-logging char-acteristics; (2) preparing different lithofacies' well-logging image datasets; and (3) establishing a CNN model with training, testing, and verification. For lithofacies recognition in a tight sandstone reservoir, compared with the recognized result of a model based on a well-logging vector datasets, such as decision tree analysis (DTA; 83%), support vector machine (SVM; 81%), K-nearest neighbor (KNN; 86%), and random forest (RF; 92%), the CNN model has a better prediction accuracy (92%). It also performs well when recognizing new wells in the Sanzhao Sag (92.16%). This result shows that the CNN model can effectively identify tight sandstone reservoir lithofacies based on a well-logging image dataset. Additionally, the sample intervals, lithofacies, geologic characteristics, and sample numbers affecting the prediction accuracy of the CNN model were also analyzed.