Identification of Karst caves in seismic data based on deep convolutional neural network
YAN Xingyu1,2, LI Zongjie3, GU Hanming1,2, CHEN Benchi4, DENG Guangxiao3, LIU Jun3
1. Institute of Geophysics&Geomatics, China University of Geosciences(Wuhan), Wuhan, Hubei 430074, China; 2. Hubei Subsurface Multiscale Image Key Laboratory, Wuhan, Hubei 430074, China; 3. Research Institute of Exploration and Development, Northwest Oilfield Branch Co., SINOPEC, Urumqi, Xinjiang 830011, China; 4. Oil Field Department of Science and Technology Ministry, SINOPEC, Beijing 100728, China
Abstract：Karst cave identification is significant for the exploration and development of fracture-cavity oil and gas reservoirs. Conventional identification methods are multi-solution and inefficient. Therefore, a deep learning method with strong feature learning and generalization capabilities is introduced into Karst cave identification. However, it is still a challenging task to identify Karst caves by deep learning due to the complex response characteristics of Karst caves to the seismic wavefield, the small sizes of anomalies, and the difficulties in obtaining training samples. Faced with this pro-blem, we propose a "two-step" deep learning me-thod for identifying Karst caves in seismic data. Spe-cifically, the U-Net model is used to identify the "bead-shaped" anomalous reflection on the seismic section. Then, according to the identification results of the "bead-shaped" anomalies, seismic data are cropped into small patches and input into the deep residual network to implement the prediction of the actual Karst cave profile. Considering the difficulties in obtaining training data for actual Karst cave prediction, we propose implementing wave equation forward modeling to generate seismic Karst cave data with accurate labels. The application of field seismic data shows that the me-thod is accurate in Karst cave identification, has strong noise resistance, and can greatly save the cost of manual interpretation.
闫星宇, 李宗杰, 顾汉明, 陈本池, 邓光校, 刘军. 基于深度卷积神经网络的地震数据溶洞识别[J]. 石油地球物理勘探, 2022, 57(1): 1-11.
YAN Xingyu, LI Zongjie, GU Hanming, CHEN Benchi, DENG Guangxiao, LIU Jun. Identification of Karst caves in seismic data based on deep convolutional neural network. Oil Geophysical Prospecting, 2022, 57(1): 1-11.
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