An automatic identification method of imbalanced lithology based on Deep Forest and K-means SMOTE

Abstract

Accurate identification of lithology is an important basis for oil and gas exploration and reservoir geological evaluation. Logging parameters often have a complex nonlinear relationship with lithology. With the development of artificial intelligence technology, a variety of data mining algorithms have been applied to lithology identification with logging data. However, due to the constraints of practical conditions, the number of labeled lithology samples is small and the imbalance between classes is obvious, which usually affect the results of lithology identification. In this paper, we proposed a hybrid unbalanced lithology identification method based on Deep Forest and K-means SMOTE to solve the above mentioned problems. Deep Forest is the first deep model of a non-differential form base learner which can perform layer-by-layer processing and feature enhancement. Compared with traditional shallow model, ensemble model and deep neural networks, it has the best performance with the accuracy of 96.17%. K-means SMOTE oversampling is able to filter subclusters and balance the dataset by oversampling only within safe regions, reducing interference from noise points and boundary ambiguity. We achieved further performance improvements in minority lithology classification via K-means SMOTE oversampling with the accuracy of 97.17%. The comprehensive evaluation results showed that the method proposed in this paper has a good effect on the identification of unbalanced lithology, and has a practical application prospect.