Bayesian Stochastic Inversion of Seismic Data in a Stratigraphic Grid

We present an efficient stochastic seismic inversion technique aimed at overcoming the band-limited nature of deterministic inversion methods by generating multiple realisations of elastic properties in fine scale stratigraphic grids. Our method uses a Bayesian framework and a linearised, weak contrast approximation of the Zoeppritz equation to estimate a log-Gaussian posterior distribution for P- and S-wave impedances. This distribution is constrained to reproduce the observed seismic data, within specified noise-dependent tolerance limits and to also honour conditioning well data. After elastic inversion, multiple realisations of P- wave and S-wave impedances can be used for cascaded stochastic simulation of petrophysical reservoir properties, lithology classification and uncertainty analysis. The technique has been successfully tested on different real data sets and we demonstrate results on a large model of more than 30 millions grid cells.