A practical fuzzy interpolator for prediction of reservoir permeability

We propose a practical fuzzy interpolator (PFI) to represent imprecise relationships between inputs and outputs in high-dimensional data systems. The method employs expert knowledge and sample data to dynamically generate piecewise linear inference rules, and then the values to be estimated are interpolated and extrapolated based on these rules. We demonstrate the use of this methodology in petroleum reservoir engineering where the permeability is estimated among oil wells. The results are compared to a neural-fuzzy technique for the same petroleum reservoir data set. This shows that the PFI is not only simple, and computationally fast, but also gives better performance than the neural-fuzzy technique.