We have developed an efficient methodology for Bayesian prediction of lithology and pore fluid, and layer-bounding horizons, in which we include and use spatial geologic prior knowledge such as vertical ordering of stratigraphic layers, possible lithologies and fluids within each stratigraphic layer, and layer thicknesses. The solution includes probabilities for lithologies and fluids and horizons and their associated uncertainties. The computational cost related to the inversion of large-scale, spatially coupled models is a severe challenge. Our approach is to evaluate all possible lithology and fluid configurations within a local neighborhood around each sample point and combine these into a consistent result for the complete trace. We use a one-step nonstationary Markov prior model for lithology and fluid probabilities. This enables prediction of horizon times, which we couple laterally to decrease the uncertainty. We have tested the algorithm on a synthetic case, in which we compare the inverted lithology and fluid probabilities to results from other algorithms. We have also run the algorithm on a real case, in which we find that we can make high-resolution predictions of horizons, even for horizons within tuning distance from each other. The methodology gives accurate predictions and has a performance making it suitable for full-field inversions.

We present a case of seismic reservoir characterization within a multi-source depositional setting. To resolve issues of nonuniqueness between intrinsic reservoir properties and the seismic data, we use a probabilistic approach. Stochastic rock physics relationships are used to establish a link between elastic parameters and various lithology classes. We highlight the importance of concurrently using multiple lithological prior models to achieve an improved understanding of the seismic response of the Edvard Grieg field. Application of a one-step inversion approach, which assesses the direct conditional (posterior) distribution of elastic parameters, yields improved reservoir prediction particularly in regions where the reservoir sandstones are thin. Quantitative prediction and interpretation of impedances and lithology volumes is done in light of selected models and associated uncertainties.

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