A significant practical problem with the pilot point method is to choose the location of the pilot points. We present a method that is intended to relieve the modeller from much of this responsibility. The basic idea is that a very large number of pilot points are distributed more or less uniformly over the model area. Singular value decomposition (SVD) of the normal matrix is used to reduce the large number of pilot point parameters to a smaller number of so-called super parameters that can be estimated by nonlinear regression from the available observations. A number of Eigenvectors corresponding to significant Eigen values (resulting from the decomposition) is used to transform the model from having many pilot point parameters to having a few super parameters. A synthetic case model is used to analyse and demonstrate the application of the presented method of model parameterization and calibration.