We propose a simple and cost-effective approach to obtain 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, model-based cluster analysis and discriminant analysis is used to identify and characterize electrofacies types. Second, we apply non-parametric 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 time-consuming. 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.
Permeability estimates are a critical aspect of a reservoir description. In sandstone reservoirs, a linear relationship normally exists between porosity and the logarithm of permeability. Thus, permeability predictions in sandstones can be achieved with acceptable accuracy using porosity from well logs. In carbonates, however, petrophysical variations rooted in diagenesis, grain size variation, cementation, etc. can significantly alter the direct relationship between porosity and permeability.1 Statistical regression has been proposed as a more versatile solution to the problem of permeability estimation. Conventional statistical regression has generally been done parametrically using multiple linear or nonlinear models.2–4
Several limitations inhibit multiple regression techniques, many arising from the inexact nature of the relationship between petrophysical variables. Conventional parametric regression requires a priori assumptions regarding functional relationships between the independent and dependent variables. In complex carbonate reservoirs such underlying physical relationships are not known in advance, making traditional multiple regression techniques inadequate and often leading to biased estimates.5–6
A variety of approaches have been proposed to partition well log responses into distinct classes in order to improve permeability predictions. The simplest approach utilizes flow zones or reservoir layering.4,6 Other approaches have used lithofacies information identified from cores and also the concept of hydraulic flow units (HFU's).7–11 However, in carbonate reservoirs such classification is complicated by the extreme petrophysical variations rooted in diagenesis and complex pore geometry even within a single zone or class. A major difficulty in this regard has been discrimination of classes from well logs in uncored wells.12,13