Schiehallion is a Palaeocene-age oil field located 175 km west of Shetland in the North Atlantic Ocean. Accurate mapping of lithofacies in this mature field is vital for continuous development of the reservoir model and for identification of infill drilling opportunities. Our new 1D stochastic inversion (ODiSI) tool can be used to estimate reservoir properties of interest (e.g., volume of shale and porosity), with associated uncertainties on these quantities (in the form of standard deviations at each sample in the inverted data sets). In addition, ODiSI outputs a set of possible lithofacies profiles at each trace location in the data set. We have outlined our initial attempts to use these profiles to generate a single lithofacies estimate volume over a small area of the original inversion. The resulting lithofacies estimate volume clearly indicates a geologically plausible distribution of the four lithofacies modeled in the field (shale, sand, shaley sand, and cemented sand) in a lateral sense. However, unlike the inputs to the estimate, the vertical distribution of facies returned is only reasonably consistent with the lithofacies logs observed at the available well control. From this, we have concluded that the process of deriving a lithofacies estimate from the ODiSI outputs needs further development.
Estimation of reservoir properties and facies from seismic data is a well-established technique, and there are numerous methods in common usage. Our 1D stochastic inversion process (ODiSI), based on matching large numbers of pseudowells to color-inverted angle stacks, produces good estimations of reservoir properties, facies probabilities, and associated uncertainties. Historically, ODiSI has only been applied to siliciclastic reservoir intervals. However, the technique is equally suited to carbonate reservoirs, and ODiSI gives good results for the Mishrif Reservoir interval in the Rumaila Field in Iraq. Of course, a thorough awareness of the quality of all input well data and detailed validation of the parameters input to the inversion process is crucial to understanding the accuracy of the results.
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