A Bayesian model for lithology/fluid class prediction using a Markov mesh prior fitted from a training image

TL;DR

This paper presents a Bayesian inversion method for lithology/fluid classification from AVO data, comparing a simple Markov random field prior with a more complex Markov mesh prior that better captures geological connectivity.

Contribution

It introduces a Markov mesh prior fitted from a training image for improved modeling of lithology/fluid connectivity in Bayesian inversion.

Findings

Markov mesh prior better captures connectivity and curvature.

Prior choice influences posterior connectivity more than marginal probabilities.

Markov mesh prior yields more geologically plausible posterior realizations.

Abstract

We consider a Bayesian model for inversion of observed amplitude variation with offset (AVO) data into lithology/fluid classes, and study in particular how the choice of prior distribution for the lithology/fluid classes influences the inversion results. Two distinct prior distributions are considered, a simple manually specified Markov random field prior with a first order neighborhood and a Markov mesh model with a much larger neighborhood estimated from a training image. They are chosen to model both horisontal connectivity and vertical thickness distribution of the lithology/fluid classes, and are compared on an offshore clastic oil reservoir in the North Sea. We combine both priors with the same linearised Gaussian likelihood function based on a convolved linearised Zoeppritz relation and estimate properties of the resulting two posterior distributions by simulating from these distributions with the Metropolis-Hastings algorithm. The influence of the prior on the marginal posterior probabilities for the lithology/fluid classes is clearly observable, but modest. The importance of the prior on the connectivity properties in the posterior realisations, however, is much stronger. The larger neighborhood of the Markov mesh prior enables it to identify and model connectivity and curvature much better than what can be done by the first order neighborhood Markov random field prior. As a result, we conclude that the posterior realisations based on the Markov mesh prior appear with much higher lateral connectivity, which is geologically plausible.