Publications

A typical 3D seismic data always suffer from poor acquisition sampling along one or more spatial dimension. Although interpolation is never a substitute of acquisition, nevertheless interpolation in one or more dimensions (3D/4D/5D) has proven to be quite successful in recent years. Binning and interpolation to common offset vectors (COV) is a recent methodology to minimize offset and azimuth variations that is favorable to Pre-stack migration algorithms. However, traditional common offset vectors (COV) parameters depend completely on shot line and receiver line spacing. An acquisition design with coarse shot-line and receiver-line spacing may lead to lack of near offsets while forming COV’s. In this work, we compare the drawback and effectiveness of 4D common offset interpolation and 5D common offset vectors (COV) interpolation. Combining them together may give best of both worlds in terms of data quality, data size and computational time. We call this combined 4D/5D interpolation technique as hybrid common offset vector (Hybrid COV) approach. This approach makes sure the missing near offset of COVs is filled by the 4D common offset interpolation, thereby making the shallow data more interpretable. The application of Hybrid COV is shown to recently acquired NW Tunu transition zone data. The data goes through hybrid COV interpolation followed by Pre-stack Kirchhoff time migration and post-migration processing. The intermediate and end results are compared with those of 4D common offset interpolation and migration processing. The Hybrid COV results look promising in terms of gather event continuity from near to far offsets. This leads to better far and ultra-far stacks results that are used for our AVO studies and finding potential sweet spots for future drilling.