Research on reservoir lithology prediction method based on convolutional recurrent neural network. (October 2021)

• Record Type: Journal Article
• Authors: Li, Kewen; Xi, Yingjie; Su, Zhaoxin; Zhu, Jianbing; Wang, Baosan
• Is Part Of: Computers & electrical engineering. Volume 95(2021)
• Publication Date: 2021-10
• Subjects: Prediction of reservoir lithology -- Seismic attributes -- Convolutional neural network -- Long short-term memory Neural network; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107404
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A model possessing the local information aggregation ability of CNN and the sequence feature extraction ability of RNN is designed to mine the implicit relationship between seismic attributes and reservoir lithology. A SEidentity_Block is designed to improve the feature extraction capability and establish the mapping relationship between seismic attributes and lithologic labels. The long distance information correlations of seismic data are mined by using Bi-LSTM model. Abstract: Considering that conventional reservoir prediction methods cannot fully explore the implicit relationship between seismic attributes and reservoir lithology, a deep learning lithology prediction model combining convolutional neural network and Long Short-Term Memory recurrent neural network is proposed to improve the classification prediction accuracy of reservoir lithology. This model is built and trained by seismic attribute data and lithology data of Shengli Oilfield to establish the nonlinear mapping relationships between seismic attributes and lithology labels. The experimental results show that the proposed method can significantly improve the effect of reservoir lithology prediction, whose prediction accuracy for complex reservoirs is close to 70%. Graphical abstract: Image, graphical abstract