Surface-Related and Internal Multiple Elimination Using Deep Learning

Peinan Bao, Ying Shi, Weihong Wang, Jialiang Xu and Xuebao Guo
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Peinan Bao: School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China
Ying Shi: Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Ministry of Education, Northeast Petroleum University, Daqing 163318, China
Weihong Wang: School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China
Jialiang Xu: School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China
Xuebao Guo: School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China

Energies, 2022, vol. 15, issue 11, 1-21

Abstract: Multiple elimination has always been a key, challenge, and hotspot in the field of hydrocarbon exploration. However, each multiple elimination method comes with one or more limitations at present. The efficiency and success of each approach strongly depend on their corresponding prior assumptions, in particular for seismic data acquired from complex geological regions. The multiple elimination approach using deep learning encodes the input seismic data to multiple levels of abstraction and decodes those levels to reconstruct the primaries without multiples. In this study, we employ a classic convolution neural network (CNN) with a U-shaped architecture which uses extremely few seismic data for end-to-end training, strongly increasing the neural network speed. Then, we apply the trained network to predict all seismic data, which solves the problem of difficult elimination of global multiples, avoids the regularization of seismic data, and reduces massive amounts of calculation in traditional methods. Several synthetic and field experiments are conducted to validate the advantages of the trained network model. The results indicate that the model has the powerful generalization ability and high calculation efficiency for removing surface-related multiples and internal multiples effectively.

Keywords: multiple; surface-related and internal multiple; deep learning; elimination (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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