Abstract

Most of the shear-wave anisotropy inversion methods are based on the waveform similarity of split fast and slow shear waves, employing an optimization algorithm to solve the derived nonlinear objective function. Although current methods can decrease the ambiguity in determining the fast shear-wave azimuth, the assumption of waveform similarity can hardly be satisfied in field cases, leading to inaccurate anisotropy estimations. In addition, due to the influence of borehole conditions and formation properties, the crossed-dipole data gathered from the field are often mixed with leaky P-wave in slow formations. These factors may severely degrade the reliability of the anisotropy inversion. In this paper, we propose an algorithm based on a shift-invariant multiscale analysis tool, dual-tree complex wavelet transform and slowness-time correlograms (STC) to suppress the leaky P-wave that could not be removed by the band-pass filter. Subsequently, the main characteristics of the flexural wave can be more clearly extracted to enhance the waveform similarity in multiple time-frequency domains. Finally, a multi-objective inversion function is introduced to further reduce the ambiguity in shear-wave anisotropy inversion. The applications of this algorithm to synthetic data and field data indicate more stable and accurate inversion results compared with current methods, especially in formations with weak anisotropy.

Introduction

Tang and Chunduru (1999) presented a processing method to measure anisotropy (transverse isotropy horizontal TIH type) from crossed-dipole data, which employs a waveform inversion analysis. This method can simultaneously obtain the magnitude and azimuth of formation anisotropy by finding the global minimum of the constructed objective function using the very fast simulated-annealing (VFSA) method (Ingber, 1989). Compared to previous methods (Alford, 1986; Igel and Crampin, 1990; Li and Crampin, 1993), waveform inversion analysis can decrease the ambiguity in determining the anisotropy azimuth by stacking the data using all array receiver combinations (Tang and Patterson, 2001). But the measured flexural arrival should satisfy the assumption of waveform similarity, which means that the received waveforms have similar shape only with different arrival times.

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