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Seismic modelling for reservoir studies: a comparison between convolutional and full‐waveform methods for a deep‐water turbidite sandstone reservoir

Two seismic modelling approaches, that is, two‐dimensional pre‐stack elastic finite‐difference and one‐dimensional convolution methods, are compared in a modelling exercise over the fluid‐flow simulation model of a producing deep‐water turbidite sandstone reservoir in the West of Shetland Basin. If the appropriate parameterization for one‐dimensional convolution is used, the differences in three‐dimensional and four‐dimensional seismic responses from the two methods are negligible. The key parameters to ensure an accurate seismic response are a representative wavelet, the distribution of common‐depth points and their associated angles of incidence. Conventional seismic images generated by the one‐dimensional convolutional model suffer from lack of continuity because it only accounts for vertical resolution. After application of a lateral resolution function, the convolutional and finite‐difference seismic images are very similar. Although transmission effects, internal multiples and P‐to‐S conversions are not included in our convolutional modelling, the subtle differences between images from the two methods indicates that such effects are of secondary nature in our study. A quantitative comparison of the (normalized root‐mean‐square) amplitude attributes and waveform kinematics indicates that the finite‐difference approach does not offer any tangible benefit in our target‐oriented seismic modelling case study, and the potential errors from one‐dimensional convolution modelling are comparatively much smaller than the production‐induced time‐lapse changes.