Conditioning of three-dimensional generative adversarial networks for pore and reservoir-scale models
This work addresses the need for efficient conditional geostatistical modeling in oil and gas reservoir studies, representing an incremental advancement over existing GAN-based methods.
The paper tackles the problem of generating conditional 3D pore- and reservoir-scale models using generative adversarial networks (GANs), extending previous unconditional methods to incorporate conditioning data through content and perceptual losses, and demonstrates its effectiveness on micro-CT images of Ketton limestone and Maules Creek aquifer simulations.
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.