  Geostatistical modeling of petrophysical properties is a key step in modern
integrated oil and gas reservoir studies. Recently, generative adversarial
networks (GAN) have been shown to be a successful method for generating
unconditional simulations of pore- and reservoir-scale models. This
contribution leverages the differentiable nature of neural networks to extend
GANs to the conditional simulation of three-dimensional pore- and
reservoir-scale models. Based on the previous work of Yeh et al. (2016), we use
a content loss to constrain to the conditioning data and a perceptual loss
obtained from the evaluation of the GAN discriminator network. The technique is
tested on the generation of three-dimensional micro-CT images of a Ketton
limestone constrained by two-dimensional cross-sections, and on the simulation
of the Maules Creek alluvial aquifer constrained by one-dimensional sections.
Our results show that GANs represent a powerful method for sampling conditioned
pore and reservoir samples for stochastic reservoir evaluation workflows.
