Geophysical monitoring of subsurface reservoirs relies on detecting small changes in the seismic response between a baseline and monitor study. However, internal multiples, related to the over- and underburden, can obstruct the view of the target response, hence complicating the time-lapse analysis. In order to retrieve a response that is free from over- and underburden effects, the data-driven Marchenko method is used. This method effectively isolates the target response, which can then be used to extract more precise time-lapse changes. Additionally, the method also reveals target-related multiples that probe the reservoir more than once, which further define the changes in the reservoir. To verify the effectiveness of the method, a numerical example is constructed. This test shows that when using the isolated target response, the observed time differences resemble the expected time differences in the reservoir. Moreover, the results obtained with target-related multiples also benefit from the Marchenko-based isolation of the reservoir. It is, therefore, concluded that this method has the potential to observe dynamic changes in the subsurface with increased accuracy.
Discerning small time-lapse traveltime changes by isolating the seismic response of a reservoir using the Marchenko method IJsseldijk, Johno van; Wapenaar, Kees
Marchenko imaging is based on integral representations for focusing functions and Green’s functions. In practice, the integrals are replaced by finite summations. This works well for regularly sampled data, but the quality of the results degrades in a case of imperfect sampling. We have developed discrete representations that account for imperfect sampling of the sources (or, via reciprocity, of the receivers). These representations contain point-spread functions that explain the blurring of the focusing functions and Green’s functions due to imperfect sampling. Deblurring the focusing functions and Green’s functions involves multidimensional deconvolution for the point-spread functions. The discrete representations form the basis for modifying Marchenko imaging to account for imperfectly sampled data, which is important for field data applications.
Summary
The Marchenko method retrieves the responses to virtual sources in the Earth’s subsurface from reflection data at the surface, accounting for all orders of multiple reflections. The method is based on two integral representations for focusing- and Green’s functions. In discretized form, these integrals are represented by finite summations over the acquisition geometry. Consequently, the method requires ideal geometries of regularly sampled and co-located sources and receivers. Recently new representations were derived, which handle imperfectly sampled data. These new representations use point-spread functions (PSFs) that reconstruct results as if they were acquired using a perfect geometry. Here, the iterative Marchenko scheme is adapted, using these new representations, to account for imperfect sampling. This new methodology is tested on a 2D numerical data example. The results show clear improvement of the proposed scheme over the standard iterative scheme. By removing the requirement for perfect geometries, the Marchenko method can be more widely applied to field data.
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