We propose a simple, cost-effective approach to obtaining permeability estimates in heterogeneous carbonate reservoirs using commonly available well logs. Our approach follows a two-step procedure. First, we classify the well-log data into electrofacies types. This classification does not require any artificial subdivision of the data population but follows naturally based on the unique characteristics of well-log measurements, reflecting minerals and lithofacies within the logged interval. A combination of principal component analysis (PCA), model-based cluster analysis (MCA), and discriminant analysis is used to identify and characterize electrofacies types. Second, we apply nonparametric regression techniques to predict permeability using well logs within each electrofacies.
Our proposed method has been successfully applied to the North Robertson Unit (NRU) in Gaines county, west Texas. Previous attempts to derive permeability correlations at the NRU have included rock-type identification using thin-section and pore-geometry analysis that can sometimes be expensive and timeconsuming. The proposed approach resulted in improved permeability estimates, leading to an enhanced reservoir characterization, and can potentially benefit both daily operations and reservoir simulation efforts. The successful field application demonstrates that the electrofacies classification used in conjunction with sound geologic interpretation can significantly improve reservoir descriptions in complex carbonate reservoirs.