Oil Logging Reservoir Recognition Based on TCN and SA-BiLSTM Deep Learning Method. (May 2023)

• Record Type: Journal Article
• Authors: Yang, Wenbiao; Xia, Kewen; Fan, Shurui
• Is Part Of: Engineering applications of artificial intelligence. Volume 121(2023)
• Publication Date: 2023-05
• Subjects: Logging interpretation -- Temporal Convolutional Network -- Self-Attention mechanism -- Bidirectional Long Short Time Memory network -- Logging reservoir recognition; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2023.105950
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The use of Deep Learning methods to mine useful and critical information from massive and complex logging datasets is of great importance for oil logging reservoir recognition. TCN-SA-BiLSTM was proposed due to the lack of previous studies to mine the internal correlation of the features of the logging dataset. TCN-SA-BiLSTM is a deep learning model that hybridizes Temporal Convolutional Network (TCN), Self-Attention mechanism (SA), and Bidirectional Long Short Term Memory network (BiLSTM). First, for the pre-processed feature data, TCN is used for feature extraction with parallel convolution operation. Then, by exploiting the ability of SA to extract the internal autocorrelation of time series features, this can better capture the dependence of feature data over long distances. Finally, the contextual linkage of the features is further obtained using BiLSTM. The experimental results show that TCN-SA-BiLSTM exhibits excellent performance in comparison with seven competing models on all performance evaluation metrics. It overcomes the deficiencies in capability exhibited by traditional logging interpretation techniques to improve the efficiency and success rate of oil and gas exploration. Graphical abstract: