Summary
The identification and mapping of rock facies is important to reliable reservoir characterization. Traditionally, facies identification and mapping are based on inspection of core data and/or well-log signatures, a procedure that has subjective aspects because it is relies on samples from only a very small portion of the reservoir. Such identification is also difficult to perform at the onset of the exploration stage because of lack of sufficient well data. This paper demonstrates a simple practical approach to identify and classify facies from seismic-amplitude data using basic statistical concepts.
Within a geologic facies, measured properties [in this case acoustic impedance (AI)] are assumed to differ mainly as a result of random additive events and are modeled by a normal distribution, as justified by the central-limit theorem. The facies are identified by estimating the combination of facies volume fractions and distribution parameters (means and standard deviations of the facies probability-density function) that best fit the population distribution of AI. A simple form of Bayes theorem is then used to compute the probability of occurrence of each of the facies at the measured locations. This generates a volume of facies probabilities corresponding to the seismic volume. Such a volume can be used to perform facies-specific petrophysical analysis or be a starting point to generate multiple realizations of petrophysical properties. The approach is simple and transparent to use, with no significant computational requirements even on large data sets.
We describe an application of the procedure to a synthetic reference data set and a Gulf of Mexico AI data set. Mapped probabilities of the individual facies show the spatial continuity and geologic character of the underlying depositional environment. Property values within the mapped regions are substantially less variable than the original data across the entire region. The within-facies semivariograms exhibit much less spatial correlation than across all facies. Since the facies are mapped across an exhaustive data volume, this approach considerably reduces the need for the geostatistical construction of property distributions within them as long as a high correlation exists between the seismic attribute and petrophysical properties.
