Paper designed to focus on new technology - our specific focus is on the use of modern processing sequences applied to 2D legacy data in Peru (GeoSpec). There are links drawn from previous geological study of the area.
Main authors are BP and CGG are co-authors so limited on what updates can be made. Ongoing thoughts are that CGG would like more acknowledgment for the "EVO"; approach but other than that happy with the 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, multi-modal surface wave inversion (MM-SWI) was used to build high resolution shallow S-wave velocity model for one of the recently acquired Ocean Bottom Node (OBN) surveys. To generate high signal-to-noise ratio (S/N) dispersion spectrum for MM-SWI, super gathers based on the extracted surface waves were constructed. Aperture (within 400 m) was carefully chosen to ensure dispersion spectrum is of high spatial resolution as well. Auto-picking of multi-modal dispersion curves was implemented after the dispersion analysis. To overcome the strong nonlinear problem during multi-modal inversion, the combined Levenberg-Marquard (LM) and differential evolution (DE) inversion method was used. High resolution dispersion analysis and robust auto-picking of multi-modal dispersion curves enhance productivity significantly which allows a high resolution shallow S wave velocity model to be built efficiently for this survey. The inverted MM-SWI model correlates well with both the shallow geological structures and the seafloor map. It is able to reveal the fine layers, small faults, small channels and other lithological variations. PS statics was then computed from the inverted high resolution Vs model and applied to improve the continuity and coherency of the PS time image. The MM-SWI model was also incorporated into the S-wave velocity model building process for PS depth imaging. The evidences from the model comparison, statics application and PS depth image support the reliability of the high resolution shallow S-wave velocity model from MM-SWI.
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 short timeframe, a high degree of technical content had to be adopted by employing a robust pre-processing sequence, including up-down deconvolution, along with an iterative approach to the velocity model build, utilising GWI, FWI, high-density tomography and anisotropic information derived from the PS data.
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 interval in an otherwise isotropic overburden. The velocity model was updated with focus on reducing parameter cross-talk using 2-parameter Vp/epsilon joint FWI, then Vp/Q joint FWI. The FWI is run with combined OBN and towed-streamer input data, using a shot weighting strategy to balance the contributions to the gradient and global cost.
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, pre-stack multiple modelling and attenuation performed with variations of the predictive deconvolution and correlation-convolution methods produced accurate multiple models that undergo little distortion during adaption. The methods appear safe for pre-stack use and for both structural and AVO analysis.
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 frequency. The synthetic data from the Marmousi model as well as one challenging OBC dataset from Indonesia are used to demonstrate this method.
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 a 3-parameter FWI workflow addressing velocity, absorption factor (Q), and epsilon. This improves the imaging and positioning of complex injectite structures around 2.5 km depth. We also seek an increased level of detail in the final velocity model for use as a background to AVO inversion.
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 help to mitigate this problem, but applications are generally limited to 2D or work on one sailline at a time. We describe a 3D multi-sailline deconvolution-like approach that uses surface datum deghosted data to derive a multiple prediction operator in the image domain using one-way wavefield extrapolation operators. Synthetic and real data results show how the method may predict multiples from shallow multiple generators and may outperform targeted demultiple approaches.