Time-Lapse Seismic Processing Algorithms

Wavelet matching, spatial regularization, and survey matching

Our dedicated algorithms for time-lapse seismic processing perform wavelet matching, spatial regularization, and survey matching to address issues identified by our initial and comprehensive QC steps.

Wavelet matching

Wavelet matching algorithms are handled deterministically, where possible, with residual matching by statistical methods. Constrained and frequency-variant matching algorithms can be applied as global operators or local filters with appropriate smoothing. Frequency-variant operators can be constrained to operate over only the bandwidth in which the surveys differ. This ensures that the surveys have very similar wavelets by the end of the processing flow.

Get faster results: Workflows created using our algorithms are customized for your monitor survey datasets to reduce processing turnaround time.

Spatial regularization

Efficient interpretation of time-lapse seismic datasets requires that equivalent traces of those datasets have identical spatial coordinates. Our multidimensional regularization algorithms accurately interpolate the prestack data from their acquired midpoint locations to an identically sampled grid for each survey. Time-lapse binning ensures that the regularized datasets are created from the best repeated trace pairs of the base and monitor surveys. Acquisition geometry errors or inaccurate navigation data are compensated by spatial repositioning and offset error correction workflows. These ensure that nominally collocated traces are indeed in the same spatial locations.

Survey matching

Survey amplitude, timing, and phase attributes vary across each survey and are identified and compensated for using our flexible attribute decomposition algorithms or by normalized root-mean-square (NRMS) scanning. These processes are often crucial to ensuring that acquisition variability does not obscure the 4D signal.