Machine learning

An Active Learning strategy for a well-known image classification problem, classically used in Machine Learning benchmarks.
ELK stack with a database / indexation technique for quick searches / dashboards and views.
An innovative method to automatically generate maps of prospectivity, based on Machine Learning and multicriteria decision analysis.
A web-based solution, which transforms cement bond logs images into sets of values and interprets them in terms of cement quality in a semi-automatic fashion.
The TELLUS team looks forward to meeting you at EAGE Digital in Paris, France, March 25-27.
Can the pressure answer of a field be estimated with neural networks in situations of fast decision-making or when a reservoir model is not available?
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
Document classification is one of the major parts of the manual effort, especially when the documents to classify are scattered within a huge database
The representation of geological objects in micromodels is often insufficiently realistic
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
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