q-energy-hackathon-i / Large 3D image generation using GAN for geoscience

Generating large 3D images can be useful in many applications from geoscience to medical imagery to battery modelling etc... Here we focus on a geoscience application where the 3D images correspond to possible realizations of some subsurface properties such as the permeability or the rock type.

Our objective is to use GAN for dimensional reduction to solve an inverse problem. In fact the subsurface model is very uncertain: we have some geostatistical tools that can generate different possible images but we need to find those who are in accordance with surface data (some observations wells). The application of this method could range from oil & gas to hydrogeology to CO2 geological storage. The challenge we want to focus here is to build a GAN method that scale well with image size in terms of GPU memory and in terms of training time. Another essential feature of the solution is that the latent space should be kept reasonably small (<100) as this will be used in a subsequent inverse problem solution.