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.

Marine multicomponent sea‐floor data of excellent quality have been acquired over the Tommeliten Alpha field. The most dominating wave modes are interpreted to be conventional compressional PP-waves and converted PS-waves. The most important geophysical problem associated with the Tommeliten Alpha field is the presence of a gas chimney obscuring the conventional 3-D seismic image of the reservoir zone. The converted PS-waves effectively undershoot the gas chimney, leading to substantially improved images of the reservoir. Subsequent interpretation indicates the Tommeliten Alpha structure is a faulted dome.

In this paper we describe the highlights from a wide range of CSEM applications and developments in the Wisting area. At an initial stage, by including higher frequencies in 3D CSEM inversion at Wisting, we realized that our CSEM data contained a lot more detailed information about reservoir properties than earlier anticipated. Beyond the traditional application of predicting high vs. low hydrocarbon saturation, the CSEM data are used for estimation of hydrocarbon column and is starting to be used in estimation of reservoir heterogeneity and even connectivity. Our quantitative workflows are still maturing and are expected to provide future value. At Wisting we have been fortunate to be in an active appraisal setting where new wells have repeatedly provided calibration and adjustment to our CSEM workflows. During almost four years we have acquired two field-scale tailored 3D CSEM surveys with gradually denser spatial sampling and higher frequencies. These have provided higher accuracy and better spatial resolution than the conventional coarse-grid survey design used in multi-client projects. Our project work has been highly cross-disciplinary, where CSEM expertise paired with specialists in rock physics, seismic AVO and geology has worked very well. Our ability to operate as one team across company barriers is a key success factor with learning, re-learning and geoscience integration as main ingredients.

Abstract. Reliably predicting lithologic and saturation heterogeneities is a key problem in reservoir characterization. This study shows how near and far offset seismic impedance can be used to classify lithologies and pore fluids. The near offset seismic stack approximates a zero offset section, giving an estimate of the normal incidence acoustic impedance (AI = pV). The far offset stack gives an estimate of a Vp/Vs related elastic impedance (EI)attribute, equivalent to the acoustic impedance for non-normal incidence. These attributes can be computed from log data. Well data can be used, prior to seismic inversions, to test the feasibility of using AI-EI attributes for lithofacies identification.Bi-variate AI-EI probability distribution functions can be estimated from logs to obtain classification success rate for each facies. Success rate is a measure of the value of far-offset data for reservoir characterization. Examples are presented from North Sea, Gulf of Mexico, and Australia.

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