Seismic facies clustering technology based on deep embedding network
LI Qixin1, LUO Yaneng2, MA Xiaoqiang1, CHEN Cheng1, ZHU Yanhe1
1. CNOOC Research Institute Co., Ltd., Beijing 100028, China; 2. Geophysical Research & Development Center, BGP Inc., CNPC, Zhuozhou, Hebei 072751, China
Abstract:The early seismic facies clustering based on machine learning relies on the selection and combination of seismic attributes, leading to strong subjectivity of results. Nevertheless, this defect can be overcome by data-driven deep learning. Therefore,with deep learning technology, the autoencoder network is adopted to generate embedding code that can be used for abstract representation of seismic data. The clustering loss function and the reconstruction loss function are introduced to build a combined loss function, which is then optimized so that the seismic features learned can not only be used to reconstruct seismic data but also have favorable clustering ability. The proposed method is applied to a tight gas exploration area A in Ordos Basin. The following observations are drawn from the results: After 500 iterations, the embedding code has noticeable clustering features, and the original seismic signals can be well reconstructed with a relative error of less than 5%; compared with that in the case of the root-mean-square (RMS) amplitude attribute, the seismic facies map calculated by the seismic facies clustering technology based on deep embedding network delineates channels more accurately with richer detail; compared with the K-means clustering algorithm, the proposed technology delivers a prediction result that has a higher coincidence rate between seismic data and well logging data, which can reach 89.3%.
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