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We provide a unique range of technologies, services and equipment designed to deliver geoscience solutions right across the Exploration & Production life cycle.
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Operating safely and with integrity in order to deliver sustainable performance.
CGG recognizes the 10 UNGC principles and publishes annually its communication on progress.
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Explore an extraordinary opportunity to help create the future solutions of geoscience challenges.
Our range of geoscience experience is unparalleled and leads us to invent and perfect technology and techniques unlike any other.
Our aim is to build a rebalanced geoscience group that is firmly anchored in its unique and high-tech positions in Equipment, Imaging-Reservoir and high-end Data Acquisition.
GeoTraining brings together the full breadth of CGG’s skill development programs to provide the E&P industry with comprehensive geoscience workforce learning path programs.
Expert, independent advice on the most appropriate satellite imagery (optical, radar) and elevation data products.
The benchmark for petrophysics, rock physics, facies analysis and statistical mineralogy. Collaborative multi-well log analysis made easy for better drilling decisions.
Emerge is a geostatistical, attribute prediction module that can predict property volumes using well logs and attributes from seismic data. The predicted properties can be any log types available, such as porosity, velocity, density, gamma-ray, lithology and water saturation. Emerge can also be used to predict missing logs or parts of logs by using existing logs that are common to the available wells.
Using multi-linear regression or neural network analysis, Emerge trains itself at the well locations to learn the optimum transform that relates the logs and seismic data. It then applies that transform to derive a volume of the log property from the seismic volume(s).
Emerge log predict uses the same multi-attribute methodology as seismic attribute prediction, but applies it to log data. It can predict missing logs or parts of logs not by using seismic data, but by using existing logs that are common to the wells in the training data set.
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