Four-component rotation and fast-slow wave separation techniques for 3D vector S-wave seismic data of S-wave sources
GONG Ting1, WANG Zhaolei1, LUO Wenshan1, WANG Yongsheng2, YUE Yuan-yuan3, GU Xiaodi1
1. Geophysical Research Institute, BGP Inc., CNPC, Zhuozhou, Hebei 072750, China; 2. Exploration Department of Qinghai Oilfield Company, PetroChina, Dunhuang, Gansun 736202, China; 3. BGP Research and Development Center, CNPC, Zhuozhou, Hebei 072750, China
Abstract：Shear wave (S-wave) exploration has great advantages in solving problems such as structural imaging in gas cloud areas and fine description of lithological oil-gas reservoirs. To address the problem of poor-quality of P-wave data in the Sanhu area of Qaidam Basin, BGP carries out a 3D multi-wave exploration experiment for the first time, which is jointly excited by P-wave and S-wave vibroseis and yields the 3D seismic data of nine components. In this study, we explore the four-component seismic data from the nine-component 3D seismic data, which are excited by the S-wave sources and received by the horizontal components of S-wave geophones. Different from P-waves, S-waves enjoy obvious vector characteristics. Previous research on S-wave processing technology was based on 2D S-wave processing or 3D converted wave seismic data processing, and the vector relationship of the complex wave field in 3D of S-waves was insufficiently considered; thus, it could not be applied to 3D S-wave seismic data. Through continuous research and exploration, supporting four-component processing techniques of vector S-waves with four-component rotation and fast-slow wave separation at the core are primarily formed, including vector rotation of four S-wave components, static correction, pre-stack gather processing, fracture azimuth determination, fast and slow wave separation, velocity analysis, and pre-stack migration imaging technology. These supporting techniques effectively solve the vector processing problem in S-wave exploration and obtain high-quality S-wave imaging results with a signal-to-noise ratio (SNR) comparable to that of P-waves. They boast great advantages in structural imaging in gas cloud areas and micro-fracture identification, which are essential for the prediction of lithologic reservoirs in the Sanhu area.
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GONG Ting, WANG Zhaolei, LUO Wenshan, WANG Yongsheng, YUE Yuan-yuan, GU Xiaodi. Four-component rotation and fast-slow wave separation techniques for 3D vector S-wave seismic data of S-wave sources. Oil Geophysical Prospecting, 2022, 57(5): 1028-1034.
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