Prestack seismic inversion driven by mixture probabilistic models
LI Kun1, YIN Xingyao1,2
1. School of Geosciences, China University of Petroleum(East China), Qingdao, Shandong 266580, China; 2. Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong 266071, China
Abstract:Prestack seismic inversion is the most important method for quantitatively evaluate the elastic,lithologic and fluid properties of subsurface media. Conventional seismic inversion often seperately predicts the ‘elastic modulus’,‘discrete lithology’ and ‘fluid factor’ while ignoring the influence of lithology on model parameters,so that the errors of prior knowledge introduced will seriously affect the accuracy of seismic inversion and lithology prediction. Considering that the prior probability density function (PDF) of model parameters follows the distributon of mixture probabilistic density,this paper derives the explicit solution to the posterior PDF under the constraints of four conditional datasets including time-domain seismic data,frequency-domain seismic data,composite low-frequency prior informaton and known model points on the Bayesian framework. After introducing non-linear boundary constraints into prestack elastic inverson,the solution to model inversion becomes stable. The posterior PDF is randomly sampled by the sequential simulation algorithm,and the simulation results of different posterior probability components are classified,consequently a prestack simultaneous prediction method is established for continuous ‘elastic parameters’,‘discrete lithology’ and ‘fluid factor’.Model and real data have proved the method effectivee and practical.
作者简介: 李坤,讲师,博士后,1989年生;2013、2016和2019年分别获中国石油大学(华东)勘查地球物理专业学士、地质工程专业硕士和地质资源与地质工程专业博士学位;现为SEG、EAGE、CGS协会会员,担任《Geophysics》、《Journal of Applied Geophysics》、《Journal of Geophysics and Engineering》及《石油物探》等专业期刊的审稿人;现就职于中国石油大学(华东),主要从事勘探地球物理反演与油气识别等领域的教学与科研。
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