Bayesian lithology/fluid prediction and simulation on the basis of a Markov-chain prior model

Abstract

A technique for lithology/fluid (LF) prediction and simulation from prestack seismic data is developed in a Bayesian framework. The objective is to determine the LF classes along 1D profiles through a reservoir target zone. A stationary Markov-chain prior model is used to model vertical continuity of LF classes along the profile. The likelihood relates the LF classes to the elastic properties and to the seismic data, and it introduces vertical correlation because the seismic data are band-limited. An approximation of the likelihood model provides an approximate posterior model that is a Markov chain. The approximate posterior can be assessed by an exact and efficient recursive algorithm. The LF inversion approach is evaluated on a synthetic 1D profile that is inspired by a North Sea sandstone reservoir. With a realistic wavelet-colored noise model and a S/N ratio of three in the seismic data, the results are reliable. The LF classes and the interfaces between zones are largely correct. The prediction uncertainty increases if the number of zones increases and zone thicknesses decreases. The study clearly demonstrates the impact of a vertically coupled prior Markov model for the LF classes.