5D anti-aliasing interpolation: Application on an unconventional shale play

Abstract

Fourier-based minimum weighted norm interpolation (MWNI) has been widely used to regularize land seismic data. It is relatively fast computationally, and easily extends to higher dimensions. However, it has difficulty interpolating regular missing data that are spatially aliased. Chiu and Anno (2012) proposed a new anti-aliasing MWNI method to overcome the aliasing artifacts in MWNI. Their interpolation scheme expands the capability of the conventional MWNI to handle aliased data that are often associated with steeply dipping structures, and produces more reliable interpolation results. A 2D synthetic data example clearly shows that anti-aliasing MWNI outperforms the conventional MWNI method. We apply it to a field data acquired from an unconventional shale play and carry out a comprehensive evaluation of interpolated data through the processes of pre-stack analyses, pre-stack time migration, and pre-stack depth migration. The 5D interpolation yields considerable uplifts to the improvements of image qualities. The pre-stack gathers after the 5D interpolation are more suitable for azimuthal AVO and velocity analysis.