Mapping Geo-Cellular Models to Unstructured Simulation Grids
Mosser, L. J. (2013, September). Mapping Geo-Cellular Models to Unstructured Simulation Grids. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.
CV
Education
- B.Sc. in Petroleum Engineering, Montanuniversitaet Leoben, 2015
- M.Sc. in Petroleum Engineering (with Distinction), Imperial College London, 2016
- Ph.D, Imperial College London, 2019
Prizes and Awards
- John S. Archer Award for Research Excellence, Imperial College London, 2018
- 1st Place in SEG Machine Learning Contest with Alfredo de la Fuente
- BP Award for Best MSc. Petroleum Engineering Thesis Project 2016
- 2nd Place SPE International Student Paper Contest (B.S. Category)
- 1st Place SPE European Paper Contest (B.S. Category)
Work experience
- : Principal Geo / Data Scientist
- Earth Science Analytics AS
- Since October 2019
- R&D Team Lead
- Research Engineer Intern
- Statoil (now Equinor), Houston, USA
- July - September 2015
- Research and Development of Hydraulic Fracturing Technologies
- : Research Assistant
- Montanuniversitaet Leoben, Austria
- January 2012 - November 2014
- Project: Stochastic Modeling of Discrete Fracture Networks
- Supervisor: Prof. Stephan Matthai
- : Research Intern in Reservoir Engineering and Software Dev
- Paradigm, Nancy, France
- July 2013 - September 2013
- : Reservoir Engineering Intern
- ExxonMobil GmbH, Hannover, Germany
- August 2012 - September 2012
- : Reservoir Engineering Intern
- RAG Austria AG, Vienna, Austria
- August 2011 - September 2011
- : Field Engineer Intern
- ExxonMobil GmbH, Grossenkneten, Germany
- July 2011 - August 2011
Publications
Tesselations Stable under Iteration
Mosser, L. J., & Matthäi, S. K. Tessellations stable under iteration.
Reconstruction of three-dimensional porous media using generative adversarial neural networks
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.'
Reconstruction of Three-Dimensional Porous Media: Statistical or Deep Learning Approach?
Mosser, L., Le Blévec, T., & Dubrule, O. (2018). Reconstruction of Three-Dimensional Porous Media: Statistical or Deep Learning Approach?. Statistical Data Science, 125.
Well-data-based discrete fracture and matrix modelling and flow-based upscaling of multilayer carbonate reservoir horizons
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.
Stochastic reconstruction of an oolitic limestone by generative adversarial networks
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.
Conditioning of Generative Adversarial Networks for Pore and Reservoir Scale Models
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.
Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks
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.
Stochastic Seismic Waveform Inversion using Generative Adversarial Networks as a Geological Prior
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.
Talks
Interpore 2017: 3D Reconstruction of Porous Media using Generative Adversarial Networks
Talk at Interpore 2017, Rotterdam, Netherlands
IAMG 2018: Stochastic Simulation with Generative Adversarial Networks
Talk at International Assosciation of Mathematical Geoscientists, Olumuc, Czech Republic
Generative Adversarial Networks as Priors for Inverse Problems
Poster at Gordon Conference: Flow and Transport in Porous Media, Maine, USA
EAGE 18: Conditioning of Generative Adversarial Networks for Pore and Reservoir-Scale Models
Talk at EAGE 18, Copenhagen, Denmark
PESGB 18: Stochastic Seismic Waveform Inversion Using Generative Adversarial Networks As A Geological Prior
Talk at 1st PESGB/EAGE Workshop on Machine Learning in Geoscience 2018, London, UK
Teaching
Service and leadership
- I serve on the EAGE AI Committee and on various technical committees for EAGE Conferences.
- I’m a member of the SEG Special Interest Group for Artificial Intelligence