Stochastic AVO inversion driven by geofluid facies and DE-MCMC model
- Kun Li (China University of Petroleum (East China)) | Xingyao Yin (China University of Petroleum (East China)) | Zhaoyun Zong (China University of Petroleum (East China))
- Document ID
- Society of Exploration Geophysicists
- SEG International Exposition and Annual Meeting, 15-20 September, San Antonio, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Exploration Geophysicists
- Facies, AVO/AVA, Interpretation, Inversion, Reservoir characterization
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- 8 since 2007
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Reservoir characterization based on amplitude variation with offsets/angles (AVO/AVA) is the core of geophysics, which focuses on the quantitative prediction of lithology and pore geofluid properties. In this study, we have proposed one simultaneous stochastic inversion approach for discrete fluid-facies and continuous geofluid parameters based on differential evolution Markov Chain Monte Carlo (DE-MCMC) algorithm and Gaussian mixture model. The Gaussian mixture model influenced by reservoir pore fluid-facies is assumed to be the prior probability density distribution of elastic parameters in Bayesian inference. The likelihood probability function is first established with time-frequency joint domain seismic data. Then, the standard posterior probability distribution expressed with a multi-dimensional Gaussian mixture model is derived. Due to the superiority of multi-solution global optimization in DE-MCMC model, the DE-MCMC model is first introduced into the optimization of mixture posterior PDF. The fluid-facies is predicted effectively through the posterior probabilities of discrete facies. The main advantage of DE-MCMC AVO inversion is that the probability distributions of estimated parameters can be easily built based on the multiple-solutions optimization. Thus, the uncertainty and reliability of geofluid parameters and fluid-facies are assessed effectively. Model and field application examples demonstrate the feasibility and stability of the proposed methodology evidently.
Presentation Date: Monday, September 16, 2019
Session Start Time: 1:50 PM
Presentation Time: 3:55 PM
Location: Poster Station 8
Presentation Type: Poster
|File Size||944 KB||Number of Pages||5|
Fjeldstad,T., andD.Grana,2018,Joint probabilistic petrophysics-seismic inversion based on Gaussian mixture and Markov chain prior models:Geophysics,83,no.1,R31–R42, doi:10.1190/geo2017-0239.1.
Grana,D.,T.Fjeldstad, andH.Omre,2017,Bayesian Gaussian mixture linear inversion for geophysical inverse problems:Mathematical Geosciences,49,493–515, doi:10.1007/s11004-016-9671-9.
Lang,X. Z., andD.Grana,2017,Geostatistical inversion of prestack seismic data for the joint estimation of facies and impedances using stochastic sampling from Gaussian mixture posterior distributions:Geophysics,82,no.4,M55–M65, doi:10.1190/geo2016-0670.1.
Li,K.,X. Y.Yin, andZ. Y.Zong,2017,Pre-stack Bayesian cascade AVA inversion in complex-Laplace domain and its application to the broadband data acquired at East China:Journal of Petroleum Science and Engineering,158,751–765, doi:10.1016/j.petrol.2017.09.005.