Annonce en lien avec l’Action/le Réseau : aucun

Laboratoire/Entreprise : TOTAL – Pau
Durée : 3 ans
Contact : mark.asch@u-picardie.fr
Date limite de publication : 2017-10-30

Contexte :
Traditional tools used to make forecasts and optimize oil reservoirs production are based on complex geological modeling of the subsurface and on computationally expensive numerical solvers of the fluid flow equations in porous media. This approach enables the integration of all the available data (seismic, cores samples, wells logs, geological and physical understanding), however it is very time consuming, moreover, the model obtained is usually not predictive enough for the short-term production optimization particularly for large and mature fields.
More recently a different paradigm based on data-driven reservoir modeling has emerged that aims at providing faster results using data analytics. This approach has the advantage of being much faster and easy to implement, however validating and trusting the model forecasts is usually more controversial.

Sujet :
Voir document joint.
See attached document.

Profil du candidat :
Good programming skills, either in numerical solution of pde’s and/or deep learning methods. Knowledge of data science is a plus.

Formation et compétences requises :
Master in a computationally related domain or in data science.

Adresse d’emploi :
TOTAL SA
Avenue Larribau,
64000 Pau France

Document attaché : ThèseFastReservoirForecaster.pdf