  This work explores a novel approach for adaptive, differentiable
parametrization of large-scale non-stationary random fields. Coupled with any
gradient-based algorithm, the method can be applied to variety of optimization
problems, including history matching. The developed technique is based on
principal component analysis (PCA), but, in contrast to other PCA-based
methods, allows to amend parametrization process regarding objective function
behaviour.
