We propose a two-step approach to permeability prediction from well logs that uses nonparametric regression in conjunction with multivariate statistical analysis. First, we classify the well-log data into electrofacies types. This classification does not require any artificial subdivision of the data population; it follows naturally based on the unique characteristics of well-log measurements reflecting minerals and lithofacies within the logged interval. A combination of principal components analysis (PCA), model-based cluster analysis (MCA), and discriminant analysis is used to characterize and identify electrofacies types. Second, we apply nonparametric regression techniques to predict permeability using well logs within each electrofacies. Three nonparametric approaches are examined—alternating conditional expectations (ACE), generalized additive model (GAM), and neural networks (NNET)—and the relative advantages and disadvantages are explored.

We have applied the proposed technique to the Salt Creek Field Unit (SCFU), a highly heterogeneous carbonate reservoir in the Permian Basin, west Texas. The results are compared with three other approaches to permeability predictions that use data partitioning based on reservoir layering, lithofacies information, and hydraulic flow units (HFUs). An examination of the error rates associated with discriminant analysis for uncored wells indicates that data classification based on electrofacies characterization is more robust compared to other approaches. For permeability predictions, the ACE model appears to outperform the other nonparametric approaches.

You can access this article if you purchase or spend a download.