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Lithology/fluid prediction is phrased in a Bayesian setting, based on prestack seismic data and well observations. The likelihood model contains a convolved linearized Zoeppritz relation and rock physics models with depth trends caused by compaction and cementation. Well observations are assumed to be exact. The likelihood model contains several global… (More)

- Kjartan Rimstad, Henning Omre
- Computational Statistics & Data Analysis
- 2013

A convolutional two-level hidden Markov model is defined and evaluated. The bottom level contains an unobserved categorical Markov chain, and given the variables in this level the middle level contains unobserved conditionally independent Gaussian variables. The top level contains observable variables that are a convolution of the variables in the middle… (More)

Skewness is often present in a wide range of spatial prediction problems, and modeling it in the spatial context remains a challenging problem. In this study a skew-Gaussian random field is considered. The skew-Gaussian random field is constructed by using the multivariate closed skew-normal distribution, which is a generalization of the traditional normal… (More)

We study non-Gaussian random fields constructed by the selection normal distribution , and we term them selection Gaussian random fields. The selection Gaussian random field can capture skewness, multi-modality, and to some extend heavy tails in the marginal distribution. We present a Metropolis-Hastings algorithm for efficient simulation of realizations… (More)

In this study rock physics depth trends and a lithology/fluid Markov random field are used to constrain a 2D Bayesian lithology/fluid inversion. Lithology/fluid classes are defined in a lithol-ogy/porosity/fluid space, and a stochastic relation from lithology/porosity/fluids to prestack seismic data is established. The rock physics depth trends are modeled… (More)

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