Intelligent interpretation and modeling method of velocity semblance based on Bayesian decision theory
WU Guofu1, XIAO Mingtu2, WANG Huazhong1, LING Yue2, ZHAO Yuhe2
1. School of Ocean and Earth Science, Tongji University, Shanghai 200092, China; 2. Research Institute of Exploration and Development-Northwest, PetroChina, Lanzhou, Gansu 730020, China
Abstract：As a strong nonlinear problem,the high precision velocity modeling requires a relatively correct initial velocity model. The initial background velocity sacnning method based on the common middle-point (CMP) gathers is the most robust method. Facing the huge size of CMP gathers,it is necessary to research the intelligent initial background velocity analysis method,and its core is the reasonable velocity semblance interpretation. This can be viewed as a scan for the most reasonable time-velocity(TV) pairs with the smallest value of defined risk decision function in a high-dimensional velocity semblance data based on the prior knowledge of the interpreters and the horizon constraint in the sense of Baye-sian decision. Therefore,this paper proposes a decision framework guided by the logic in manual interaction picking. Firstly,the semblance data and “pseudo stack profile” are generated to further extract the structural information from the profile by calculating the coherence properties. Then,K-means clustering is performed on the semblance panel according to the structural information. For each classification,TV pairs that minimize the cost function constrained by priori information and statistical information from the data space are iteratively searched. Finally,the velocity field is formed by interpolation and smoothing,as the lateral discontinuities are reduced by quality control based on statistics constraints. This method implements the utilization of horizon information into the whole process from clustering to automatic pi-cking,and quantifies the priori knowledge of the interpreter and neighborhood picking results into restrained variables in automatic picking. This can reflect the intelligence of velocity semblance interpretation and shorten the velocity modeling cycle.
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