Machine learning

The representation of geological objects in micromodels is often insufficiently realistic
They presented early results and good practices on the practical use of Deep Learning approaches to operational data sets of geological images
Lithological interpretation of core samples is a decisive early stage of many geoscience workflows
TELLUS Lab Team presented "Deep Learning Applications to Unstructured Geological Data: From Rock Images Characterization to Scientific Literature Mining"
A joint publication between the TELLUS team at IFPEN and IFP School addresses the benefits of emergent digital technologies
For mature fields, a long history of production data is often available, but the impact of geological factors remains hard to assess
Well logs interpretation is often rather subjective and highly time-consuming
Accurately detecting and locating a large number of objects of interest in thin section images is an arduous task
Quantitative analyzes of thin sections often imply tedious searches and counts of specific elements such as micro-fossils
Identification of lithological types from rock samples is cornerstone in many subsurface activities
IFP Energies Nouvelles (IFPEN) and UNESCO have signed a framework partnership agreement
The Mineral Exploration Symposium was co-organized by the European Association of Geoscientists and Engineers (EAGE) and the European Commission
Research for Integrative Numerical Geology (RING) is an international research consortium dedicated to geomodelling and quantiative geosciences