Wide-azimuth processing for azimuthal anisotropy analysis Cynthia Gomez, Erika Angerer*, CGG Technology London 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 Rüger’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 independent. Statics Residual static shifts have a significant impact on the reliability of amplitude-based anisotropy attributes. Figure 2a shows an overlay of the intensity or magnitude of the inverted ellipses (in colour) with the noise-free intercept trace (wiggle). Figure 2b is the overlay with the orientation of the maxima of the inverted ellipses as the colour attribute. Without static problems the maxima of the inverted intensity correspond to the maxima in the data and the orientation is consistent in time. Figures 2c and 2d show the corresponding overlays in the presence of residual static shifts that are up to 20% of the dominant wavelength. In Figure 2c, the intensity maxima occur at the zero-crossings of the intercept trace. Moreover, in Figure 2d, the inverted orientation shows cyclic 90° variations introduced by random as well as systematic static shifts. Even small static shifts have a significant impact on the inverted anisotropic attributes. However, static problems are readily identified by the described effects in Figures 2c and 2d. In order to remove static problems consistently within the entire volume, the data need to be processed continuously. In the case of a limited-azimuth distribution (e.g. shot point along the edge of the survey), there is a potential of introducing an azimuthal bias to the surface-consistent statics solution. Imaging and velocities Prestack imaging is performed on each azimuth sector separately keeping the parameterisation constant. Importantly, this approach corrects for the azimuthal effects introduced by lateral velocity variations (i.e. dipping reflectors). Dipping reflectors produce sinusoidal azimuthal traveltime variations that interfere with azimuthal anisotropy attributes extracted prior to imaging. Azimuthal residual moveout analysis is performed on azimuth-sectored imaged data. Spectral balancing For the extraction of amplitude-based attributes spectral variations in offset and azimuth need to be compensated for. These variations can be caused by moveout stretch, differential attenuation, and acquisition effects. A

Azimuth-preserved processing

controlled approach to compensate these effects is to balance the power, amplitude or signal spectra of partial azimuth and offset/incidence stacks. Data examples of anisotropy attributes Following the results of synthetic tests, we applied an azimuth-friendly processing sequence to a Middle East data set. Here, we compare anisotropy attributes derived from azimuthal AVO and azimuthal elastic impedance inversion. Figure 3 shows the inverted anisotropy intensity and orientation of a crossline. The maxima of the intensity coincide with peaks and troughs in the data and the orientations are vertically and laterally consistent confirming a reliable processing sequence. Figure 4 shows the derived anisotropy intensity maps of interval velocities of a 150 ms interval and (b) the intensity derived from azimuthal AVO along the base of the interval. In general, there is good agreement between the two maps for zones of both high and low anisotropy. A zone of high anisotropy, marked by red boxes occurs in both attributes, thereby increasing the confidence in this anomaly. Figure 5 shows orientation-intensity maps along the same horizon as in Figure 4 derived from (a) azimuthal AVO and (b) azimuthal impedance inversion as presented by Angerer et al. (2003) using a layer-based, 3D algorithm. Again, both maps show consistent trends. However, the impedance inversion result shows improved lateral coherence and a reduced standard deviation of the inverted anisotropy parameters. Summary Table 1 gives an overview as to whether to apply standard processing techniques to wide-azimuth data with the data treated as a single volume or separated in sectors. It becomes evident that azimuth-sectoring is part of any amplitude- and traveltime-preserving processing sequence. This forms then the basis for the extraction of reliable anisotropy parameters, which are then ready to be integrated in a fractured reservoir characterisation workflow. References Rüger, A., 1998, Variation of P-wave reflectivity with offset and azimuth in anisotropic media, Geophysics, Vol. 63, No. 3, 935-947. Angerer, E., Lanfranchi, P., and Rogers, S., 2003, Fractured reservoir modeling from seismic to simulator – A reality?, TLE, 684-689.

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τ-p transform Statics Prestack imaging Trace-by-trace gains,

scalars, filters Spectral balancing Table 1: Overview of azimuth-sectored and continuous processing techniques

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Figure 1: Amplitude preservation of FX deconvolution on wide-azimuth data; solid lines: exact azimuthal amplitudes of noise-free synthetic test data; dashed lines: data with added random noise (S/N=0.5) and application of FX deconvolution in (a) azimuth-sectored gathers and (b) in unsectored (mixed azimuth) gathers; incidence angles: 5° (red), 15° (green), 25° (blue), 35° (magenta).

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Figure 2: Overlay of inverted anisotropy parameters and intercept trace; (a) intensity (blue (low) – red (high)) and (b) orientation without static shifts; (c) intensity and (d) with static shifts.

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Figure 3: (a) Intensity (white (low) – red (high)) and (b) orientation attributes of a cross-section. The red circle marks a spatially and vertically consistent high anisotropy zone. Input data is shown as wiggle display.

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Figure 5: Orientation and intensity horizon displays; (a) derived from azimuthal AVO and (b) derived from azimuthal elastic impedance inversion; background map: normalised fitting error (white (low) – blue (high)).

Figure 4: Intensity horizon displays; (a) derived from azimuthal interval velocity analysis and (b) derived from azimuthal AVO along the base of the interval: colour scale: white (low) – blue (high). Red box denotes high anisotropy zones occurring in both attributes.

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