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Processing and imaging time-lapse datasets requires great care and attention to detail which is why our experience really makes a difference. The aim of 4D processing is to attenuate the 4D noise caused by changes in acquisition parameters or environmental conditions, and to emphasise the 4D signature of the reservoir caused by changes in fluid, pressure and stress.
A key philosophy in 4D processing is to optimize for the 4D difference. For example with 4D demultiple, if you optimize for best multiple attenuation on each individual dataset you usually end up with a worse 4D result. We use 4D cost functions to allow a trade-off between what is best for processing and what gives the best 4D difference.
Static differences between vintages occur in both land and marine 4D surveys. They are caused by changes in the near-surface, weathering layer, water column or water surface conditions. Our range of strategies and algorithms include application of deterministic GPS-based tidal and waveheight corrections and statistical 4D corrections.
The regularization of 4D datasets uses bin centring and interpolation of missing data to create either regularly sampled datasets on a common grid, or to map monitor survey data onto a base survey grid. Our REVIVE technique uses up to 5 dimensions to dramatically increase the interpolation accuracy.
4D binning is an essential step in time-lapse processing which selects the most compatible data for 4D processing. Traces are selected from all of the vintages making up the 4D dataset which are best matched in terms of source, receiver and midpoint position, offset and azimuth. The binning criteria can be extended to include statistical measurements of 4D data quality such as cross-correlation, predictability and NRMS.
Systematic amplitude variations related to the acquisition geometry create a pattern or acquisition footprint. The footprint within each vintage is emphasized in the 4D difference. We use a 4D de-striping technique where data from all vintages are analyzed for systematic variations with respect to acquisition attributes, such as sail line, and scalars are calculated to compensate for them.
Matching is used to minimize residual 4D differences caused by acquisition and environmental variations which cannot be addressed by deterministic corrections. We have a range of sophisticated multi-vintage matching techniques to suit a wide range of 4D datasets, including the merging and 4D matching of streamer and OBC datasets.
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