The problem of mismatch between repeated time-lapse seismic surveys remains a challenge, particularly for land acquisition. In this dissertation, we present a new algorithm, which is an extension of the surface-consistent model, and which minimizes the mismatch between surveys, hence improving repeatability.
We introduce the concept of surface-consistent matching .lters (SCMF) for processing time-lapse seismic data, where matching .lters are convolutional .lters 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 datasets 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 .rst designing trace-sequential, least-squares matching .lters, then Fourier transforming them. A subsequent least-squares solution then factors the trace-sequential matching filters into four opera\255tors: two surface-consistent (source and receiver), and two subsurface-consistent (o.set and midpoint).
We apply the algorithm to two datasets: a synthetic time-lapse model and .eld data from a CO2 monitoring site in Northern Alberta. In addition, two common time-lapse processing schemes (independent processing and simultaneous processing) are compared. We present a modi.cation of the simultaneous processing scheme as a direct result of applying the new SCMF algorithm. The results of applying the SCMF together with the new modified simultaneous processing .ow reveal the potential bene.t of the method, however some challenges remain, speci.cally in the presence of random noise.
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