Semi-supervised seismic facies analysis based on prestack seismic texture

被引：0
|
作者：
Cai H. [1 ,2 ]
Hu H.-Y. [1 ]
Wu Q. [1 ]
Wang J. [3 ]
Li Z. [3 ]
机构：
[1] School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan
[2] Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan
[3] Research Institute of Exploration & Production, SINOPEC Shengli Oilfield, Dongying, 257015, Shandong
来源：
Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting | 2020年 / 55卷 / 03期
关键词：
Pre-stack texture; Seismic facies analysis; Self-organizing map; Semi-supervised learning;
D O I：
10.13810/j.cnki.issn.1000-7210.2020.03.003
中图分类号：
学科分类号：
摘要：
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. © 2020, Editorial Department OIL GEOPHYSICAL PROSPECTING. All right reserved.
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页码：504 / 509
页数：5
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