Parametrization and generation of geological models with generative adversarial networks
This work addresses geological modeling for subsurface flow problems, representing an incremental application of existing GAN methods to a new domain.
The authors tackled the challenge of capturing complex geological structures in subsurface models by applying Wasserstein GANs for parametrization and generation, showing that the generated samples preserve multipoint statistical features and reproduce geological structures and flow statistics.
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.