Surface-consistent matching filters for time-lapse seismic processing

Mahdi H Almutlaq and Gary F. Margrave


We introduce the concept of surface-consistent matching filters for processing time- lapse seismic data, where matching filters are convolutional filters that minimize the sum- squared error between two signals. Since in the Fourier domain, a matching filter is the spectral ratio of the two signals, we extend the well known surface-consistent hypothesis such that the data term is a trace-by-trace spectral ratio of two data sets instead of only one (i.e. surface-consistent deconvolution). To avoid unstable division of spectra, we compute the spectral ratios in the time domain by first designing trace-sequential, least-squares matching filters, then Fourier transforming them. A subsequent least-squares solution then factors the trace-sequential matching filters into four operators : two surface-consistent (source and receiver), and two subsurface-consistent (offset and midpoint).

We present a time-lapse synthetic data set with nonrepeatable acquisition parameters, complex near surface geology, and a variable subsurface reservoir layer. We compute the four-operator surface-consistent matching filters from two surveys, baseline and monitor, then apply these matching filters to the monitor survey to match it to the baseline survey over a temporal window where changes are not expected. This algorithm significantly reduces the effect of most of the nonrepeatable parameters, such as differences in source strength, receiver coupling, wavelet bandwidth and phase, and static shifts. We compute the NRMS (normalized root mean square difference) on raw stacked data (baseline and monitor) and obtained a mean value of 70%. This value was significantly reduced after applying the four-component surface-consistent matching filters to about 15%.

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