Semi-supervised seismic facies analysis based on prestack seismic texture
CAI Hanpeng1,2, HU Hao-yang1, WU Qingping1, WANG Jun3, LI Zhipeng3
1. School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China; 2. Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China; 3. Research Institute of Exploration & Production, SINOPEC Shengli Oilfield, Dongying, Shandong 257015, China
Abstract：A semi-supervised seismic facies analysis algorithm based on prestack seismic texture is proposed for taking full use of subtle information contained in prestack seismic data based on prior knowledge such as drilling and geological data.First,prestack seismic texture is introduced to highlight the variability of tiny space and amplitude with azimuth/offset in prestack seismic data.Second,self-organizing map (SOM) is used to train samples.Finally,constrained by prior drilling knowledge,the semi-supervised clustering of neurons in the output layer of SOM is carried out to generate the mapping relation between the neurons and the seismic facies category.Theoretical model and application demonstrated that the method can improve the accuracy of seismic facies map and enhance the ability to distinguish seismic microfacies.It is a better tool for seismic facies analysis.
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