Reservoir characterization using nonparametric regression techniques

Abstract

This research presents an approach to obtain accurate and general permeability correlations in heterogeneous carbonate reservoirs. Very often, it is difficult to achieve reliable permeability correlations in these complex and heterogeneous reservoirs, but a simple and computationally efficient correlation is developed using only commonly available well log responses. Accurate permeability correlations are essential to understand, forecast, manage, and control production operations, and this method may easily be applied to field data using only a spreadsheet and some statistical software tool. Current methods are very costly and time consuming, and few, if any, simple and reliable procedures currently exist to obtain dependable permeability correlations for heterogeneous carbonate reservoirs. The power of this method is based on permeability being obtained directly from well log responses, and the results have led to enhanced reservoir characterization based on flow, or permeability, rather than storage, or porosity. However, we believe it would be very useful to apply this method in conjunction with a detailed geological description, which may yield a geological interpretation of each of the electrofacies defined by this study. This approach to improved permeability prediction uses electrofacies characterization determined from nonparametric regression and multivariate statistical analysis. Step 1 of this method is to classify the well log data into electrofacies types. For the purpose of this paper, an electrofacies is defined as a similar set of well log responses characterizing a certain rock type. A combination of principal component analysis, model-based cluster analysis and discriminant analysis is used to characterize and identify electrofacies types. Also, this thesis describes the application of nonparametric regression techniques using well logs within each electrofacies to predict permeability. One very important conclusion from this research is that electrofacies characterization improves permeability prediction in carbonate reservoirs significantly, especially when used in conjunction with nonparametric regression techniques. Also, nonparametric regression techniques have proven useful to obtain reliable correlations between well log responses and permeability, and discriminant analysis of uncored wells indicates data classification based on electrofacies to be a valid approach. Once good correlations between electrofacies and permeability are established, significant geological information can be extracted from well logs alone. This approach has been successfully applied to the North Robertson Unit (NRU), Gaines County, west Texas. In addition, the electrofacies approach proves to be very promising compared to other data partitioning methods.