Research on Reservoir Lithology Prediction Based on Residual Neural Network and Squeeze-and- Excitation Neural Network
Conventional reservoir prediction methods ar not sufficient to explore the implicit relation between seismic attributes, and thus data utilization is low. In order to improve the predictive classification accuracy of reservoir lithology, this paper proposes a deep learning lithology prediction method based on ResNet (Residual Neural Network) and SENet (Squeeze-and-Excitation Neural Network). The neural network model is built and trained by using seismic attribute data and lithology data of Shengli oilfield, and the nonlinear mapping relationship between seismic attribute and lithology marker is established. The experimental results show that this method can significantly improve the classification effect of reservoir lithology, and the classification accuracy is close to 70%. This study can effectively predict the lithology of undrilled area and provide support for exploration and development.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 527
 Ma Zheng, Zhang Chunlei, Gao Shichen. Lithology identification based on principal component analysis and fuzzy recognition (J). Lithologic Reservoirs, 2017, 29(5):127–133.
 Zhou You, Zhang Guangzhi, Gao Gang, et al. Application of kernel principal component analysis in well logging turbidite lithology identification (J). Oil Geophysical Prospecting, 2019, 54(3):667-675.
 Yong Shihe, Logging data processing and comprehensive interpretation (M). DongYing: China University of Petroleum Press, 2007.
 Li Guohe, Zheng Yang, Li Ying, et al. Lithology recognition of multi-sampling points based on deep belief network (J). Progress in Geophysics, 2018, 33(4):1660-1665.
 An Peng, Cao Danping. Research and application of logging lithology identification based on deep learning (J). Progress in Geophysics, 2018, 33(3):1029-1034.
 Hu Jialiang, Gao Chaoyu, Yu Jifeng, et al. Lithology Identification of Unconventional Reservoirs Based on PCA-BP Neural Network (J). Journal of Shandong University of Science and Technology (Natural Science), 2016, 35(05):9-16.
 Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition (J). Proceedings of the IEEE, 1998, 86(11):2278-2324.
 Krizhevsky A, Sutskever I, Hinton G. ImageNet Classification with Deep Convolutional Neural Networks (C). NIPS. Curran Associates Inc. 2012.
 Deng L, Li J, Huang J T, et al. Recent advances in deep learning for speech research at Microsoft (J). IEEE International Conference on Acoustics. IEEE, 2013:8604-8608.
 Zhou Feiyan, Jin Linpeng, Dong Jun. Review of research on convolutional neural networks (J). Chinese Journal of Computers, 2017, 40(06):1229-1251.
 Szegedy C, Liu W, Jia Y, et al. Going Deeper with Convolutions (C). 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015.
 He Kaiming, Zhang Xiangyu, Ren Shaoqing, Sun Jian. Deep Residual Learning for Image Recognition (C). The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778.
 He Jie, Shen Li, Sun Gang. Squeeze-and-Excitation Networks (C). The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 7132-7141.
 Lin Jingdong, Wu Xinyi, Chai Yi, Yin Hongpeng. Review on structural optimization of convolutional neural networks (J). Acta Automatica Sinica:1-14(2019-08-04).
 Greff K, Srivastava R K, Koutník, Jan, et al. LSTM: A Search Space Odyssey (J). IEEE Transactions on Neural Networks & Learning Systems, 2015, 28(10):2222-2232.
 Hinton G E. Rectified Linear Units Improve Restricted Boltzmann Machines Vinod Nair (C). International Conference on International Conference on Machine Learning. Omnipress, 2010.
 Li Kewen, Zhou Guangyue, Lu Shenqiang, et al. A new method of favorable zone evaluation based on machine learning (J). Special Oil & Gas Reservoirs, 2019, 26(03):7-11.