Lithologic Reservoirs ›› 2023, Vol. 35 ›› Issue (4): 79-89.doi: 10.12108/yxyqc.20230408

• PETROLEUM EXPLORATION • Previous Articles    

Optimization of 3D first-arrival traveltime tomography inversion

XU Xin1,2, YANG Wuyang1,2, ZHANG Kai3, WEI Xinjian1,2, ZHANG Xiangyang1,2, LI Haishan1,2   

  1. 1. PetroChina Research Institute of Petroleum Exploration & Development-Northwest, Lanzhou 730020, China;
    2. Key Laboratory of Internet of Things, CNPC, Lanzhou 730020, China;
    3. School of Earth Science and Technology, China University of Petroleum(East China), Qingdao 266000, Shandong, China
  • Received:2022-06-27 Revised:2022-08-30 Published:2023-07-01

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Abstract: The 3D first-arrival traveltime tomography inversion was optimized by picking up the first-arrival wave using machine learning，establishing the initial velocity constrained by micro logs，obtaining the global traveltime by multi-stencils fast marching method and narrow-band continuation algorithm，gaining the ray propagation path by ray tracing technology，and restricting the target inversion equation by apparent slowness and regularization. The results show that： （1）The machine learning based on image segmentation was used to divide the seismic records into three categories，including the early，the later and the narrow band of first arrival. The classification was weighted to pick up 576 000 channels of seismic data. The accuracy of picking up was greater than 99%，and the efficiency of picking up was more than 80 times higher than that of manual pick up.（2）Using the multi-stencils fast marching method which includes six difference templates can cover the traveltime calculation of 26 nodes around the grid，which can improve the traveltime calculation accuracy of grid diagonal direction. The 3D narrowband extension technique could be used to calculate the forward traveltime of global grid nodes. The 3D ray tracing technique based on Runge-Kutta algorithm could obtain the ray path.（3）Using micro logs information to construct the initial velocity and using apparent slowness information and regularization technology to constrain the tomography inversion equation can effectively improve the accuracy of velocity inversion within 1 000 m underground.（4）The optimized tomography inversion technology has been used to process the actual seismic data of Fusha 4 wiring bundle and Yingmai block in Tarim Oilfield，which could eliminate the “bull's eye” illusion and boundary artifact. The velocity curve of well point is highly consistent with the actual logs data.

Key words: first-arrival traveltime, tomography inversion, first break picking, machine learning, micro-logging constraints, regularization constraints, apparent slowness constraints

CLC Number: 

  • TE319+.2
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