Ikon - Subsurface Data Management Software

1. Parametric constant phase: A pragmatic approach is to use a constant phase approximation over the entire seismic bandwidth. This uses fewer degrees of freedom than estimating a phase spectrum and has some empirical basis in that, after processing, the phase of seismic wavelets is often approximately constant across the seismic bandwidth. When only a short log length is available this approach is especially suitable as, in practice, allowing the phase to vary with frequency could be unreliable. A sophisticated least squares based method has been implemented to derive stable amplitude and phase spectra (see Zabihi Naeini et al., 2016 for details). If required, one can modify the phase at the low frequencies towards zero frequency using the approach proposed by White and Zabihi Naeini (2014).

1. Frequency domain least-squares: We use exactly the same machinery and approach as White (1980) but we modify the auto- and cross-spectra calculation. Whilst traditionally Papoulis tapers are used to taper the auto- and cross-correlation estimates and then Fourier transform, we propose the use of multi-tapering on data segments, Fourier transform and then compute the auto- and cross-spectra. The advantage of using multi-tapers is in reducing the smoothing and leakage bias and has been discussed in more details in White and Zabihi Naeini (2014) and Zabihi Naeini et al. (2016)

1. Time domain Bayesian least-squares: Gunning and Glinsky (2006) introduced a well tie algorithm formulated in the time domain as a Bayesian inverse problem. The algorithm simultaneously estimates all the wavelet coefficients and the Bayesian formulation allows one to incorporate uncertainties associated with the time-depth mapping, positioning errors and other useful parameters. This approach treats the issue of the wavelet length as a model selection problem which may be estimated via Bayesian model selection theory. However, this technique is often prone to noise and becomes even worse for broadband data in which the desired wavelet would potentially require a long wavelet to accommodate the low frequency content efficiently. To overcome the overfitting and wavelet length issue, Zabihi Naeini et al. (2016) proposed priors which are specifically targeted to handle the low frequency character of the wavelet in this algorithm. There are also functionalities to do multi- well and volume, use vertical composite trace along the well path and a whole new tool box to modify and edit wavelets.

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