Geology - Sedimentology

To address the challenges of the energy transition, the subsurface has an important role to play, both in terms of providing resources and offering storage solutions. (...) Numerical models can help gain a better understanding of the subsurface with a view to its long-term management and optimal use. Developed for a number of years now at IFPEN, initially for the petroleum industry, such models cover scales ranging from the sedimentary basin to the reservoir...

Today, the impact of climate change and human activities on the evolution of landscapes and water resources is a major challenge. Predicting it requires dedicated tools capable of evaluating, 100 years ahead, the consequences of different scenarios on watersheds and groundwater. To this end, IFPEN is developing modeling approaches targeting erosion-transport-deposition phenomena combined with surface and subsurface flows. ...

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.

Seismic imaging remains an important tool in the characterization of geological reservoirs. In the case of carbonate reservoirs, the biological origin of the sediments, as well as their transformation over time through diagenesis , results in numerous structural heterogeneities.

Carbonate reservoirs present significant heterogeneities (in terms of types and scales) associated with the biological origin of sediments c , as well as the diagenetic transformations that took place

THESIS BY XAVIER MANGENOT

THESIS BYALEXANDRE LETTÉRON