Data interpretation

Well correlations can be partially automated to efficiently consider multiple scenarios and better assess the corresponding uncertainties.
Automatic extraction of images of interest in document database can improve the efficiency of operational workflows and help professionals save time for higher-value activities
Quantifying the uncertainties associated to well-log data can benefit any decision making based on machine learning workflows where these data are used
Predictive mapping is essential to evaluate underground prospectivity in various fields, such as Oil and Gas industry, Mineral prospection or Geothermal Energy
Predicting rock properties while drilling a well, especially if at several tens of meters ahead of the drill bit, can be key to reduce the drilling risks and their associated costs
The TELLUS team presented 3 papers. You will find direct links to them on the full news.
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
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