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

2018/02/15 by Lukas Mosser, Mosser, Lukas, Olivier Dubrule +3 · 3 citations
Engineering · #Computer Vision and Pattern Recognition (cs.CV) #Enhanced Oil Recovery Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Geophysics (physics.geo-ph) #Hydraulic Fracturing and Reservoir Analysis #Machine Learning (stat.ML) #Reservoir Engineering and Simulation Methods

paper · pdf · doi:10.48550/arxiv.1802.05622

openalex publication_date 2018/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Texture Synthesis with Spatial Generative Adversarial Networks
• NIPS 2016 Tutorial: Generative Adversarial Networks
• Improved Training of Wasserstein GANs
• On the regularization of Wasserstein GANs

Cited by

• Pixel-wise Conditioning of Generative Adversarial Networks
• DeepFlow: History Matching in the Space of Deep Generative Models
• Pix2Geomodel: A Next-Generation Reservoir Geomodeling with Property-to-Property Translation

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