Predicting permeability from well logs typically involves classification of the well-log response into relatively homogeneous subgroups based on electrofacies, Lithofacies, or hydraulic flow units (HFUs). The electrofacies-based classification involves identifying clusters in the well-log response that reflect "similar" minerals and lithofacies within the logged interval. This statistical procedure is straightforward and inexpensive. However, identification of lithofacies and HFUs relies on core-data analysis and can be expensive and time-consuming. To date, no systematic study has been performed to investigate the relative merits of the three methods in terms of their ability to predict permeability in uncored wells.
The purpose of this paper is three-fold. First, we examine the interrelationship between the three approaches using a powerful and yet intuitive statistical tool called "classification-tree analysis." The tree-based method is an exploratory technique that allows for a straightforward determination of the relative importance of the well logs in identifying electrofacies, lithofacies, and HFUs. Second, we use the tree-based method to propose an approach to account for missing well logs during permeability predictions. This is a common problem encountered during field applications. Our approach follows directly from the hierarchical decision tree that visually and quantitatively illustrates the relationship between the data groupings and the individual well-log response. Finally, we demonstrate the power and utility of our approach via field applications involving permeability predictions in a highly complex carbonate reservoir, the Salt Creek Field Unit (SCFU) in west Texas. The intuitive and visual nature of the tree-classifier approach also makes it a powerful tool for communication between geologists and engineers.