Signal processing/Data science

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.

Today, characterization of geological reservoirs, a long-standing theme in petroleum exploration, becomes a base of interest for a variety of applications, such as CO2 and hydrogen storage as well as geothermal energy. In recent years, the combined use of 3D microtomography (or micro-CT ) imaging and advanced simulation techniques has allowed the emergence of a digital approach to computing the petrophysical properties of reservoir rocks (Digital Rock Physics). This represents a real complement - and in some cases an alternative - to traditional laboratory measurements.

The design of high-quality porous materials is a major challenge for the energy efficiency of industrial processes in the fields of catalysis and biocatalysis and separation and purification operations. For such applications, these materials derive their properties of interest from their specific microstructure, incorporating a large quantity of empty spaces that are organized and connected on a nanometric scale. IFPEN and Saint Gobain Research Provence (SGRP) joined forces to acquire a tool that will ultimately facilitate the development of porous materials optimized for given usages.

Numerical simulations are now widely employed in the industrial world to help design systems and predict complex phenomena. Reactive flow simulation, for example, is important for numerous applications, such as vehicle and aircraft propulsion and processes in the chemicals industry.

As a result of the deterioration in the quality of crude oils and the tightening up of environmental standards, refiners are modifying their processes in order to meet the growing demand for light

My HDR research was aimed at optimizing catalyst performance using image processing, automating the analyses and improving the quality of the information extracted from data. The knowledge and tools

Digital technologies are playing an increasingly important role in solving industrial problems. IFPEN forged a partnership with Inria. Five years later, we look back over this fruitful partnership.