Prestack seismic stochastic inversion based on statistical characteristic parameters
Wang Bao-Li♦1,2, Lin Ying 1,2, Zhang Guang-Zhi♦1,2, Yin Xing-Yao 1,2, and Zhao Chen 1,2
1. School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China.
2. Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao Shandong 266701, China.
Abstract：
In the conventional stochastic inversion method, the spatial structure information of underground strata is usually characterized by variograms. However, effectively characterizing the heterogeneity of complex strata is difficult. In this paper, multiple parameters are used to fully explore the underground formation information in the known seismic reflection and well log data. The spatial structure characteristics of complex underground reservoirs are described more comprehensively using multiple statistical characteristic parameters. We propose a prestack seismic stochastic inversion method based on prior information on statistical characteristic parameters. According to the random medium theory, this method obtains several statistical characteristic parameters from known seismic and logging data, constructs a prior information model that meets the spatial structure characteristics of the underground strata, and integrates multiparameter constraints into the likelihood function to construct the objective function. The very fast quantum annealing algorithm is used to optimize and update the objective function to obtain the fi nal inversion result. The model test shows that compared with the traditional prior information model construction method, the prior information model based on multiple parameters in this paper contains more detailed stratigraphic information, which can better describe complex underground reservoirs. A real data analysis shows that the stochastic inversion method proposed in this paper can effectively predict the geophysical characteristics of complex underground reservoirs and has a high resolution.
2021, Vol. 18 
