标题:Total organic carbon content prediction of shale reservoirs based on discrete process neural network
作者:Liu, Zhigang ;Xiao, Dianshi ;Xu, Shaohua
作者机构:[Liu, Zhigang ] School of Computer and Information Technology, Northeast Petroleum University, Daqing; 163318, China;[Xu, Shaohua ] College of Informa 更多
通讯作者:Xiao, Dianshi
来源:Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science)
出版年:2017
卷:41
期:2
页码:80-87
DOI:10.3969/j.issn.1673-5005.2017.02.009
摘要:Traditional methods in TOC fitting generally have low precision due to the effects of lithology change. In order to improve TOC fitting precision and to reduce the time cumulative error for continuous signals in the artificial neuron network, an extreme learning discrete process neural network is proposed. A vector form is used to simulate the process input in the model. The time domain aggregation for discrete data input is controlled by the parabolic interpolation using numerical integration in the discrete process neuron. Through analysis of structure of discrete process neuron, an extreme learning algorithm is proposed. The parameters of the hidden layer are randomly assigned and the Moore-Penrose generalized inverse is used to compute the output weights. The method is applied to TOC fitting and prediction usingsome logging curves which have most sensitive response for TOC. The TOC fitting results are compared with the traditional methods and other neural network. The results show that the proposed method has higher fitting precision and faster learning speed, and the predicted TOC and actual TOC have better correlations.
© 2017, Periodical Office of China University of Petroleum. All right reserved.
收录类别:EI
资源类型:期刊论文
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