Nonrigid Matching

Extracting useful information from time-lapse seismic

Calibrating, or matching, different vintages of seismic data to each other is essential in extracting useful information from time-lapse data. Ideally all variations that are due to acquisition artifacts or noise should be minimized while at the same time there should be no effect on the seismic signal differences due to production activities in the reservoir zone.

Genuine variations in travel time between seismic vintages are also important factors in 4D reservoir monitoring, as the variations indicate change in velocity caused by pressure or fluid changes within the reservoir section.

The degree to which matching is required generally depends upon how well the acquisition has been repeated from one survey to the next. Variability within or between datasets should, where possible, be compensated by appropriate processing algorithms, rather than by survey matching.

3D NRM method

Non-rigid matching (NRM) is a method which estimates the change in two-way time (TWT) of geological features between two seismic volumes, possibly acquired at two different times. The change in TWT may be due to a change in velocity in the surveyed area ("pull-down"), displacement of one or more geological features, or a change in acquisition geometry (4D "acquisition footprint").

The method, a trace-by-trace matching, operates on pairs of collocated traces from the two surveys. For each pair, a unique operator is designed to cause one trace of the pair to better match the other. A smoothness criterion is typically imposed to ensure that the operators are spatially and temporally consistent. This enhances the contrast between the seismic responses related to changes within the reservoir and the areas where changes are due to acquisition artifacts or noise.

Benefits of NRM

  • It is less sensitive to noise than 1D methods. This is because the method is controlled by user-specified 3D continuity constraints, enabling the method to filter out non-casual displacement estimates, induced by noise.
  • It will optimize the match between the volumes on a sample-by-sample basis, providing a higher-resolution estimate than methods restricted by layers defined by horizons.
  • The matching process will provide a displacement estimate with sub-sample precision, and is not constrained to solutions expressed as fractions of the sample rate, which is the result from the more common cross-correlation based methods. The accuracy of this method is restricted only by the effective bandwidth of the signal.

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3D NRM Method Realigns Events

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