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.
CGG Environmental Science has successfully completed a project with partners Matter, Brunel University London and Swansea University to characterize micropollutants and contaminants, including microplastics and synthetic fibers, captured by domestic and industrial filters.
Twelve multi-source/multi-receiver land surveys from the Carpathian foothills were reprocessed and merged using the most advanced signal processing and imaging technologies. These included an innovative denoising subtraction using a primary model from the de-migration of a clean reflectivity from PSDM as well as a high-end surface consistent deconvolution taking into account the heterogeneity of signal across the different surveys acquired with dynamite, vibroseis and airguns. A high-end velocity model workflow was followed using MWI, Tomography joint FB/RMO, HD Multi-Layer tomography and TL-FWI and an RTM was performed with enhanced weighted azimuthal illumination to accurately image the deeper subsurface.
We will be at NAPE Summit in Houston. Stop by to network and catch up with all our latest mutli-client survey developments, including the Big Mac Merge located along the Texas Gulf Coast and the Central Basin Platform in the Permian Basin..
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.