@article{(Open Science Index):https://publications.waset.org/pdf/10011245,
	  title     = {Research on Reservoir Lithology Prediction Based on Residual Neural Network and Squeeze-and- Excitation Neural Network},
	  author    = {Li Kewen and  Su Zhaoxin and  Wang Xingmou and  Zhu Jian Bing },
	  country	= {},
	  institution	= {},
	  abstract     = {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.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {14},
	  number    = {6},
	  year      = {2020},
	  pages     = {183 - 188},
	  ee        = {https://publications.waset.org/pdf/10011245},
	  url   	= {https://publications.waset.org/vol/162},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 162, 2020},
	}