Log facies recognition based on convolutional neural network
HE Xu1, LI Zhongwei1, LIU Xin1, ZHANG Tao2
1. School of Computer and Communication Engineering, China University of Petroleum(East China), Qingdao, Shandong 266580, China; 2. Geological College, Shandong University of Science and Technology, Qingdao, Shandong 266590, China
Abstract：Natural gamma data of sandy braided river delta sedimentary environments in the Gasfield F,the East China Sea is selected as the training data to construct a deep convolutional neural network,which is used for log facies identification for the first time.Four kinds of gamma ray (GR) curve shapes are selected as characteristics,and their values are converted into the image form.Several processing steps are carried out for these images such as normalization,noise addition,rotation,and grayscale turning,and then this image data set is enhanced and expanded.In this way,training and test data sets are established.After that,a convolutional neural network is trained and used to establish the log facies identification model.During the training process,dropout,local response normalization,and L2 regularization are added to limit the complexity of the model and improve the generalization ability of the model.To automatically identify superposed deposition units of different grades in logging information,different scales of wavelet basis function and extreme value segmentation are used to classify different-scale deposition units of logging data.The comparison with other algorithms demonstrates that the proposed method achieves better log facies identification.
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