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Geostatistical modeling of petrophysical properties is a key step in modernintegrated oil and gas reservoir studies. Recently, generative adversarialnetworks (GAN) have been shown to be a successful method for generatingunconditional simulations of pore- and reservoir-scale models. Thiscontribution leverages the differentiable nature of neural networks to extendGANs to the conditional simulation of three-dimensional pore- andreservoir-scale models. Based on the previous work of Yeh et al. (2016), we usea content loss to constrain to the conditioning data and a perceptual lossobtained from the evaluation of the GAN discriminator network. The technique istested on the generation of three-dimensional micro-CT images of a Kettonlimestone constrained by two-dimensional cross-sections, and on the simulationof the Maules Creek alluvial aquifer constrained by one-dimensional sections.Our results show that GANs represent a powerful method for sampling conditionedpore and reservoir samples for stochastic reservoir evaluation workflows.

Conditioning of three-dimensional generative adversarial networks for pore and reservoir-scale models