1887

Abstract

Summary

We present a Bayesian inversion methodology for seismic reservoir characterization studies. The goal of the method is to predict rock and fluid properties and facies given a set of seismic data or amplitudes and their posterior probability distribution. The joint distribution of facies and petrophysical properties is modelled using a mixture of non-parametric distributions. This assumption does not require any particular shape of the probability density function and allows modelling the non-Gaussian and non-unimodal behaviour of rock and fluid properties caused by the presence of different litho-fluid classes and the non-linear relations between model properties and measured data. The method provides the posterior distribution of facies and reservoir properties, the most-likely models and their associated uncertainty, and it is successfully applied to synthetic and real data.

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/content/papers/10.3997/2214-4609.201901298
2019-06-03
2024-03-28
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