Publications

Stochastic Seismic Waveform Inversion using Generative Adversarial Networks as a Geological Prior

Published in ArXiv, 2018

We combine a generative adversarial network (GAN) representing an a priori model that creates subsurface geological structures and their petrophysical properties, with the numerical solution of the PDE governing the propagation of acoustic waves within the earths interior. We perform Bayesian inversion using an approximate Metropolis-adjusted Langevin algorithm (MALA) to sample from the posterior given seismic observations.

Recommended citation: Mosser, L., Dubrule, O., Blunt, M. J. (2018). Stochastic seismic waveform inversion using generative adversarial networks as a geological prior. arXiv preprint arXiv:1806.03720. https://arxiv.org/abs/1806.03720

Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks

Published in 80th EAGE Conference and Exhibition 2018, 2018

Traditional physics-based approaches to infer sub-surface properties such as fullwaveform inversion or reflectivity inversion are time consuming and computationally expensive. We present a deep-learning technique that eliminates the need for these computationally complex methods by posing the problem as one of domain transfer.

Recommended citation: Mosser, L., Kimman, W., Dramsch, J., Purves, S., De la Fuente Briceño, A., & Ganssle, G. (2018, June). Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks. In 80th EAGE Conference and Exhibition 2018. https://arxiv.org/abs/1805.08826

Conditioning of Generative Adversarial Networks for Pore and Reservoir Scale Models

Published in 80th EAGE Conference and Exhibition 2018, 2018

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.

Recommended citation: Mosser, L., Dubrule, O., & Blunt, M. J. (2018). Conditioning of three-dimensional generative adversarial networks for pore and reservoir-scale models. arXiv preprint arXiv:1802.05622. http://www.earthdoc.org/publication/publicationdetails/?publication=92364

Stochastic reconstruction of an oolitic limestone by generative adversarial networks

Published in Transport in Porous Media, 2017

We apply a GAN-based workflow of training and image generation to an oolitic Ketton limestone micro-CT unsegmented gray-level dataset.

Recommended citation: Mosser, L., Dubrule, O., & Blunt, M. J. (2018). Stochastic reconstruction of an oolitic limestone by generative adversarial networks. Transport in Porous Media, 125(1), 81-103. https://link.springer.com/article/10.1007/s11242-018-1039-9

Well-data-based discrete fracture and matrix modelling and flow-based upscaling of multilayer carbonate reservoir horizons

Published in Geological Society, London, Special Publications, 2017

Discrete fracture and matrix (DFM) homogenization, simultaneously capturing reservoir layers and contained fractures, is an alternative to discrete fracture network (DFN) upscaling. Here, the DFM approach was applied to a fractured carbonate reservoir.

Recommended citation: Milliotte, C., Jonoud, S., Wennberg, O. P., Matthäi, S. K., Jurkiw, A., & Mosser, L. (2018). Well-data-based discrete fracture and matrix modelling and flow-based upscaling of multilayer carbonate reservoir horizons. Geological Society, London, Special Publications, 459(1), 191-210. http://sp.lyellcollection.org/content/459/1/191

Reconstruction of Three-Dimensional Porous Media: Statistical or Deep Learning Approach?

Published in Book Chapter: Statistical Data Science, 2017

We compare covariance-based approaches to deep generative models for pore-scale image reconstruction.

Recommended citation: Mosser, L., Le Blévec, T., & Dubrule, O. (2018). Reconstruction of Three-Dimensional Porous Media: Statistical or Deep Learning Approach?. Statistical Data Science, 125. https://www.worldscientific.com/doi/abs/10.1142/9781786345400_0008

Reconstruction of three-dimensional porous media using generative adversarial neural networks

Published in Physical Review E, 2017

We present a method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image data sets.

Recommended citation: Mosser, L., Dubrule, O., & Blunt, M. J. (2017). Reconstruction of three-dimensional porous media using generative adversarial neural networks. Physical Review E, 96(4), 043309.' https://link.aps.org/pdf/10.1103/PhysRevE.96.043309

Tesselations Stable under Iteration

Published in Discrete Fracture Network Engineering 2014, 2014

This contribution presents a novel stochastic model based on tessellations stable under iteration also referred to as STIT. Such models naturally incorporate important features like fracture truncation and the possibility to extend their definition to arbitrary orientation distributions.

Mapping Geo-Cellular Models to Unstructured Simulation Grids

Published in SPE Annual Technical Conference and Exhibition, 2013

This contribution presents a new method designed to populate unstructured, adaptively refined tetrahedral finite element meshes with highly resolved petrophysical properties from a commercial property modeling tool.

Recommended citation: Mosser, L. J. (2013, September). Mapping Geo-Cellular Models to Unstructured Simulation Grids. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. https://www.onepetro.org/conference-paper/SPE-167629-STU

Lukas Mosser

Publications