Bayesian stochastic inversion constrained by seismic data
Zhang Fanchang1, Xiao Zhangbo2, Yin Xingyao1
1. School of Geosciences, China University of Petroleum (East China), Qindao, Shandong 266580, China;
2. Research Institute, Shenzhen Branch, CNOOC, Guangzhou, Guangdong 510240, China
Abstract：Due to the band limitation of seismic data, conventional inversion method inevitably has the shortcomings of low resolution. In this paper, we present seismic-constrained stochastic inversion based on Bayesian theory and geological statistics. This method uses well-logging information as a condition data and is constrained by seismic data. Then it integrates information from seismic, well logs, and geostatistics into a posterior probability density function of subsurface models. The perturbed simulation based on Markov chain is used to effectively sample the posterior distribution function, and the subsurface model characteristics can be inferred by analyzing a set of the posterior samples. Test results on model and real data show that this method improves the inversion precision and is very helful for reservoir fine description.
