Technical Content

Shallow S-wave velocity model building is very important to land and Ocean Bottom Seismic (OBS) PS data processing. Surface wave, propagating along near surface, brings lots of useful information of shallow lithology which is good for near surface S-wave velocity estimation. In this study ...

The Mungo field in the North Sea is both structurally and stratigraphically complex. Despite improvements of OBC acquisition over the legacy towed streamer data interpretational challenges, particularly with respect to the salt geometry, remained. To achieve the required step-change in imaging within the project’s ...

South Arne presents a velocity-model building challenge with large gas cloud obscuring underlying chalk reservoirs. Above the gas cloud is a complex near-surface with lateral velocity (Vp) variations. Additionally, a thin layer of strong anisotropy (peak epsilon around 23 %) occupies a 200-500 m ...

Generation of inaccurate multiple models that are subsequently adapted to the data can easily cause signal damage where dip discrimination between primaries and multiples is low. This is often diagnosed by appreciable distortion of the multiple model during adaption. Using data from onshore Oman ...

We put forward an optimized process in which dynamic warping is repeated with offset dependent constraint to ensure the traveltime difference reliable for the inversion. With the better reliably detected time-shifts, we can better manage the cycle-skipping problem when starting at a relatively higher ...

Velocity model building in the shallow overburden is difficult in the central North Sea. Shallow waters is tough for reflection tomography, shallow gas pockets cause velocity errors and poor imaging and anisotropy is a guess at best with little shallow well control. We present ...

Surface related demultiple continues to be a major challenge in shallow water environments. While multiples generated by the water bottom reflection can often be adequately attenuated with targeted demultiple approaches, multiple reflections from other shallow events can be more challenging to remove. Deconvolution can ...

Illustration of how machine learning application is used for Seismic global QC and detailled clusterization of data for tailored processing. Data reduction with deep neural network shows great efficiency to limit memory issue.

Reprocessing of available seismic dataset using high-end processing technology was seen to be the most economical solution for search of deeper, pre-carbonate targets in a marginal field. The vintage data suffered from amplitude loss due to absorption by gas bodies and carbonate layers causing ...