Our experts have trained more than 150 clients in 50 countries, bringing recent real-world experience in developing every basin around the world to deliver exceptional training results across multiple disciplines.
Deep neural networks are used to characterize the porosity and permeability of the Dogger formation north-east of Paris, France, that already hosts a number of geothermal plants and is set to become even more important with the transition toward renewable energies. Due to the limited availability of well data, the networks are trained on synthetic well data generated through a combination of theoretical rock physics models and statistical simulations. The networks are applied to 5 seismic lines from the 1980s and the output reservoir properties highlight several highly porous and permeable layers.
Are you a physicist, data scientist, engineer, mathematician or problem-solver? Good, glad we have that in common! Join us in transforming real seismic data into stunning 3D images of the Earth’s subsurface. No experience? Don’t worry, we’ll show you the way.
Do you treat your code the way you want others’ code to treat you? If so, you are at the right place. We have exciting development work to do globally, including high-performance computing, imaging and reservoir algorithms, and our proprietary seismic imaging software.
You’ll play a vital role in the continual development of our geoscience analytic techniques! Machine learning engineers possess a passion and aptitude for programming and enthusiasm for analytical and problem-solving challenges.