  One of the main challenges in the parametrization of geological models is the
ability to capture complex geological structures often observed in the
subsurface. In recent years, generative adversarial networks (GAN) were
proposed as an efficient method for the generation and parametrization of
complex data, showing state-of-the-art performances in challenging computer
vision tasks such as reproducing natural images (handwritten digits, human
faces, etc.). In this work, we study the application of Wasserstein GAN for the
parametrization of geological models. The effectiveness of the method is
assessed for uncertainty propagation tasks using several test cases involving
different permeability patterns and subsurface flow problems. Results show that
GANs are able to generate samples that preserve the multipoint statistical
features of the geological models both visually and quantitatively. The
generated samples reproduce both the geological structures and the flow
statistics of the reference geology.
