Signal processing/Data science

Pour de nombreuses applications industrielles, comme le recyclage chimique des plastiques, ou encore pour assurer la compatibilité entre polymères et nouveaux carburants, il est essentiel d’anticiper les interactions entre matériaux et fluides...

Coastal sedimentary basins evolve under the effect of interactions between, on the one hand, hydroclimatic processes taking place in catchment areas, and on the other hand coastal marine processes that remodel the coastline. The evolution of these environments is naturally controlled by the climate, over different time periods (ranging from tens of years to thousands of years), through variations in sedimentary flows and erosion...

Ensuring compatibility between polymers and fluids is essential in numerous industrial sectors: in the automotive sector, for example, the resistance of materials used in the fuel supply system is a vital consideration.

Emerging in the 1990s, the notions of geoheritage and geodiversity have been receiving growing attention from academic communities, international organizations and public authorities. (...) It was in this context that, in 2020, IFPEN signed a partnership agreement with UNESCO, one of the objectives of which is to share digital tools facilitating the promotion of geoheritage and geodiversity to the general public...

A large number of simulators, whether they relate to the design of reaction processes, the evolution of oil reservoirs or combustion systems, require access to thermodynamic properties. In order to provide these properties, IFPEN has been developing a library of calculation modules, called “Carnot”, named after the famous French thermodynamics expert. These calculations, in particular those concerning phase equilibrium (also known as flash calculations), generally require the use of substantial calculation resources due to the complexity of the systems considered, and represent in many cases the most time-consuming step in the simulation process.

Semantic segmentation conducted on microscopy images is a processing operation carried out to quantify a material’s porosity and its heterogeneity. It is aimed at classifying every pixel within the image (on the basis of degree of heterogeneity and porosity). However, for some materials (such as aluminas employed for catalysis), it is very difficult or even impossible using a traditional image processing approach, since porosity differences are characterized by small contrasts and complex textural variations. One way of overcoming this obstacle is to tackle semantic segmentation via deep learning, using a convolutional neural network.

Over the last decade, deep learning applied to image analysis has rapidly developed in scope to cover numerous fields. However, its potential remains underexploited in geology, despite the fact that it is a discipline that relies to a large extent on visual interpretation. To contribute to the digital transformation of industries related to the underground environment, researchers at IFPEN have implemented deep learning in three “profession-specific contexts”, each involving different types of geological images.