Monte Carlo simulations, by taking in to account distributions of attributes instead of single average values, help to avoid the flaw of averages in interpretations of seismic attributes. Calculations based on a single “best guess” input may not give the “best guess” output. Ignoring the variability of rock properties in quantitative computations can drastically distort attribute interpretations. Monte Carlo simulations also give us confidence intervals and other measures of uncertainty. Interpretations of attributes using averages and average trends alone do not give any indication of the uncertainty due to the variability in the properties. Estimating the variability in attributes can be difficult from just visual examination of clusters in cross plots. The density of points gets obscured in cross plots. What appears to be the best choice of attributes in terms of visual separability of clusters may not be the best quantitative discriminants.

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