581 citations · 623 across the 4 of their papers we have counts for
5 papers
DeepFlow: History Matching in the Space of Deep Generative Models
Lukas Mosser, Olivier Dubrule, Martin J. Blunt
The calibration of a reservoir model with observed transient data of fluid pressures and rates is a key task in obtaining a predictive model of the flow and transport behaviour of…
Stochastic seismic waveform inversion using generative adversarial networks as a geological prior
Lukas Mosser, Olivier Dubrule, Martin J. Blunt
We present an application of deep generative models in the context of partial-differential equation (PDE) constrained inverse problems. We combine a generative adversarial network…
Conditioning of three-dimensional generative adversarial networks for pore and reservoir-scale models
Lukas Mosser, Olivier Dubrule, Martin J. Blunt
Geostatistical modeling of petrophysical properties is a key step in modern integrated oil and gas reservoir studies. Recently, generative adversarial networks (GAN) have been show…
Stochastic reconstruction of an oolitic limestone by generative adversarial networks
Lukas Mosser, Olivier Dubrule, Martin J. Blunt
Stochastic image reconstruction is a key part of modern digital rock physics and materials analysis that aims to create numerous representative samples of material micro-structures…
Reconstruction of three-dimensional porous media using generative adversarial neural networks
Lukas Mosser, Olivier Dubrule, Martin J. Blunt
To evaluate the variability of multi-phase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid struct…