Reliably predicting lithologic and saturation heterogeneities is one of the key problems in reservoir characterization. In this study, we show how statistical rock physics techniques combined with seismic information can be used to classify reservoir lithologies and pore fluids. One of the innovations was to use a seismic impedance attribute (related to the [Formula: see text] ratio) that incorporates far‐offset data, but at the same time can be practically obtained using normal incidence inversion algorithms. The methods were applied to a North Sea turbidite system. We incorporated well log measurements with calibration from core data to estimate the near‐offset and far‐offset reflectivity and impedance attributes. Multivariate probability distributions were estimated from the data to identify the attribute clusters and their separability for different facies and fluid saturations. A training data was set up using Monte Carlo simulations based on the well log—derived probability distributions. Fluid substitution by Gassmann’s equation was used to extend the training data, thus accounting for pore fluid conditions not encountered in the well. Seismic inversion of near‐offset and far‐offset stacks gave us two 3‐D cubes of impedance attributes in the interwell region. The near‐offset stack approximates a zero‐offset section, giving an estimate of the normal incidence acoustic impedance. The far offset stack gives an estimate of a [Formula: see text]‐related elastic impedance attribute that is equivalent to the acoustic impedance for non‐normal incidence. These impedance attributes obtained from seismic inversion were then used with the training probability distribution functions to predict the probability of occurrence of the different lithofacies in the interwell region. Statistical classification techniques, as well as geostatistical indicator simulations were applied on the 3‐D seismic data cube. A Markov‐Bayes technique was used to update the probabilities obtained from the seismic data by taking into account the spatial correlation as estimated from the facies indicator variograms. The final results are spatial 3‐D maps of not only the most likely facies and pore fluids, but also their occurrence probabilities. A key ingredient in this study was the exploitation of physically based seismic‐to‐reservoir property transforms optimally combined with statistical techniques.

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In the context of geophysical inversion, a petrophysical forward function provides the link between properties that are directly geophysically detectable (e.g., seismic velocities and attenuation) and rock and fluid properties. Due to heterogeneity of rock properties uncertainty of the petrophysical forward function can be high. Uncertainty in the petrophysical forward function comes from two different sources: (1) measurement uncertainty, and (2) theoretical uncertainty. Measurement uncertainty accounts for errors in the acquisition and processing of data. Theoretical uncertainty on the other hand accounts for lack of knowledge about the rock type and appropriate physical theory to describe its elastic behavior. We present a method to construct the petrophysical forward function with its associated uncertainty from the both sources indicated above. The uncertainty of the petrophysical forward function is represented by a weighted sum of Gaussian distributions. Each Gaussian represents the measurement uncertainty of one facies, and the weight of the Gaussian represents the probability of the associated facies being the correct rock type to model the elastic behavior of samples. We first apply the method by assuming no theoretical uncertainty and show that the predictions of the petrophysical forward function are biased under this assumption. Then we apply the method by considering theoretical uncertainty about the type of the facies. The results show that introducing uncertainty in the facies reduces uncertainty in the final probabilistic petrophysical forward function and removes biases from its predictions.

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