Title: Parametrization and Generation of Geological Models with Generative Adversarial Networks,
Abstract: One of the main challenges in the parametrization of geological models is the
ability to capture complex geological structures often observed in subsurface
fields. 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 properties of the
reference data.