Impact of rock-physics depth trends and Markov random fields on hierarchical Bayesian lithology/fluid prediction

Early assessments of petroleum reservoirs are usually based on seismic data and observations in a small number of wells. Decision-making concerning the reservoir will be improved if these data can be integrated and converted into a lithology/fluid map of the reservoir. We analyze lithology/fluid prediction 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 parameters such as depth trend, wavelets, and error parameters; the inference of these is an integral part of the study. The prior model is based on a profile Markov random field parameterized to capture different continuity directions for lithologies and fluids. The posterior model captures prediction and model-parameter uncertainty and is assessed by Markov-chain Monte Carlo simulation-based inference. The inversion model is evaluated on a synthetic and a real data case. It is concluded that geologically plausible lithology/fluid predictions can be made. Rock physics depth trends have influence when cementation is present and/or predictions at depth outside the well range are made. Inclusion of model-parameter uncertainty makes the prediction uncertainties more realistic.