VECTOR WAVEFIELD-SEPARATION TECHNIQUES FOR IMPROVED MULTI-COMPONENT SEISMIC EXPLORATION

Abstract

Analysis of multi-component seismic data commonly involves scalar processing of the vertical component to provide a conventional P-wave image, and scalar processing of the horizontal component(s) to yield an Swave image. A number of convincing examples now exist where such S-wave imagery has significantly enhanced hydrocarbon exploration.

There is potential to achieve cleaner P- and S-wave images by more fully exploiting the true vector nature of multi-component reflection data. The simplest form of vector analysis, termed polarisation analysis, allows identification of different wave types. It does not, however, generally lead to effective wavefield separation, due to significant interference between the different waves in a typical exploration-seismic recording.

More effective vector separation is possible if the particle-motion information from polarisation analysis is coupled with the more familiar tools of frequency and velocity filtering. Three related separation algorithms, termed MUSIC, IWSA and PIM are considered here. These techniques all utilise a parametric approach whereby wavefield slowness and polarisation are modelled simultaneously in the frequency domain.

Synthetic and ocean-bottom cable examples are used to demonstrate practical issues relating to the use of these tools. The PIM algorithm is considered to be the most generally useful of the three multi-component wavefield separation algorithms. Implementation of these tools in a highly automated production environment is considered non-trivial. Hence, it is envisaged that such vector separation schemes will have most application for specialised data processing over identified target zones. Vector wavefield separation has the potential to amplify the considerable success already achieved with integrated P- and S-wave exploration.