3D seismic data completion method based on sparse strong feature extraction
CUI Xuepeng1,2, HUANG Handong1,2, LUO Yaneng3, CHENG Suo4, HAO Yaju5, CUI Gang6
1. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum(Beijing), Beijing 102249, China; 2. College of Geophysics, China University of Petroleum(Beijing), Beijing 102249, China; 3. Geophysical Research&Development Center, BGP Inc., CNPC, Zhuozhou, Hebei 072751, China; 4. Exploration and Production Research Institute, Tarim Oilfield Company, PetroChina, Korla, Xinjiang 841000, China; 5. School of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang, Jiangxi 330013, China; 6. The First Production Plant, Huabei Oilfield Company, PetroChina, Renqiu, Hebei 062552, China
Abstract：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 L1-norm based sparse representation for feature extraction (L1-SRFE) and visual representation of the singular value decomposition results are performed. Application of the actual data shows that the predicted results of the actually drilled wells and the validation wells are in good agreement, and the method has a high accuracy of label classification.
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