Technical Content

While the subsurface geoscience skills required for geothermal exploration and development differ from those required for oil and gas, there are existing skills and technologies that are highly transferable. Moreover, as the shift for geothermal resources moves away from high-enthalpy areas and towards sedimentary ...

GeoVerse is CGG’s flagship geological multi-client database. Through a combination of advanced data organization and machine-learning processes, GeoVerse enables a step-change in data analytics and data interrogation.

GepoSpec Cameroon Terms of Reference for Proposals on free blocks

A new OBN survey was acquired over the Shenzi field in the deep water GOM. The primary goal was to leverage recent advances in FWI to improve the velocity model with an expectation that the image would benefit from the full-azimuth, long offset OBN ...

Seismic imaging using full-wavefield data that includes primary reflections, transmitted waves, and their multiples, has been the holy grail for generations of geophysicists. To be able to use the full-wavefield data effectively requires a forward modeling process to generate full-wavefield data, an inversion scheme ...

Data from all sources, at all scales, has helped to assess petroleum potential offshore Mozambique beyond the relatively small play fairway proven to date. New insights into proven and potential reservoirs, plus improved predictions of the presence, type and maturity of source rocks, have ...

A machine learning workflow can quickly, and accurately, predict mineralogy, porosity and saturation in multiple wells to better understand productive layers in unconventional oil reservoirs.

Screen license round opportunities using trusted insight into the location and characteristics of natural seepage, indicative of active petroleum systems, with CGG’s focused Malaysian data packages, available for all 13 of the 2021 bid round acreages.

A new, proprietary technique that improves production forecasts has been demonstrated on an Asian gas field. Multi-scale ensemble-based history matching (MS-EnOpt) improves production forecasting and reserve estimation, and provides engineers with an understanding of key reservoir uncertainties.