Wide‐azimuth processing for azimuthal anisotropy analysis

Abstract

Azimuth-friendly data processing is mandatory in a reliable fracture characterisation workflow. Here, the effects of standard processing methodologies in the presence of azimuthal anisotropy are investigated on synthetic and real data examples. Common techniques for signal processing, statics calculations, imaging and velocity analysis are adapted for wide-azimuth, wide-offset P-wave data. We present a general processing sequence that preserves azimuthal anisotropy. Further, we compare anisotropy parameters derived from impedance inversion and azimuthal AVO on a Middle East data example. Introduction Observable effects of azimuthal anisotropy on seismic data are of second order compared to the geological background. A careful preservation of azimuthal amplitude and travel time variations is therefore crucial. One of the main questions in processing wide-azimuth data in the presence of azimuthal anisotropy is when to process the entire volume continuously as a single data set and when to split and process the data in azimuth-limited sectors. We devised a testing sequence using synthetic, anisotropic, wide-azimuth data. Processing steps are applied to the data with and without azimuth sectoring and the preservation of anisotropy is assessed. Data processing sequence Signal processing Of specific interest are transform-based signal processing techniques like 2D and 3D FK, and FX noise suppression methods. Figure 1 shows the comparison of the resulting amplitudes before and after application of FX deconvolution to the synthetic data. The solid lines are the exact amplitudes calculated using Ruger’s elliptic approximations of the anisotropic reflection coefficient (1998). The magnitude of the azimuthal variation increases with increasing incidence angle. Figure 1a shows the best fitting ellipses to the amplitudes after adding noise (S/N=0.5) and applying FX deconvolution in azimuth sectors. Figure 1b shows the resulting amplitudes after FX deconvolution treating the data as a continuous volume and thereby mixing different azimuths. It is evident that the azimuthal content of the data is preserved when the data are filtered in azimuth sectors. There is a general shift in the ellipses that is due to the added noise. In Figure 1b amplitudes are not correctly preserved, the azimuthal variation is smeared out and therefore anisotropy is not preserved. Using the same approach we also tested 2D FK filtering and τ-p transform. The results are similar to the ones shown in Figure 1. In order to preserve azimuthal variations, transform-based signal processing algorithms need to be applied to data split or sorted into azimuth sectors. A 3D FK cone filter preserves azimuthal anisotropic variations, and for this the data are processed continuously. Moreover, surface-consistent processing steps are applied to the data set continuously since source and receiver related near-surface effects are azimuth