3D seismic data completion method based on sparse strong feature extraction

被引：0
|
作者：
Gui X. [1 ,2 ]
Huang H. [1 ,2 ]
Luo Y. [3 ]
Cheng S. [4 ]
Hao Y. [5 ]
Gui G. [6 ]
机构：
[1] State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing
[2] College of Geophysics YChina University of Petroleum (Beijing), Beijing
[3] Geophysical Research and -Development Center, BGP Inc., CNPC, Hebei, Zhuozhou
[4] Exploration and Production Research Institute, Tarim Oilfield Company, PetroChina, Xinjiang, Korla
[5] School of Geophysics and Measurement-Control Technology, East China University of Technology, Jiangxi, Nanchang
[6] Huabei Oil Field Company, The First Production Plant, PetroChina, Hebei, Renqiu
来源：
Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting | 2023年 / 58卷 / 02期
关键词：
5D visual representation; L[!sub]1[!/sub]-norm sparse strong feature extraction; sample balancing strategy; sample segmentation based on majority rule; trace acquisition technology;
D O I：
10.13810/j.cnki.issn.1000-7210.2023.02.001
中图分类号：
学科分类号：
摘要：
As machine learning technology for screening seismic data features of complex reservoirs develops, how to effectively collect and analyze seismic samples involved in seismic attribute optimization and reservoir inversion has currently become a hot research topic in the field of intelligent prediction based on seismic data. Existing methods mostly focus on improving model classification algorithms, which not only consume a lot of time for manual labeling in the production and collection of labels but also suffer from poor intra-class reliability and inter-class balance in the case of label imbalance. Therefore, a 3D seismic data completion method based on sparse strong feature extraction is proposed. First,sample segmentation based on majority rule (SSMR) is used to trace multi-scale and multi-label 3D seismic samples for collection and automatic labeling. Then,the improved label shuffling balance (ILSB) method is used to complete the data by a "2 + 1" sample broadening and balancing strategy, so as to improve the model training bias caused by unbalanced sample sampling. Finally, minimum Li -norm based sparse representation for feature extraction (Li-SRFE) and visual representation of the singular value decomposition results are performed. Application of the actual data shows that the predicted results of the actual-ly drilled wells and the validation wells are in good agreement, and the method has a high accuracy of label classification. © 2023 Science Press. All rights reserved.
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页码：263 / 276
页数：13
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