Well tie for broadband seismic data

Abstract

The seismic industry is increasingly acquiring broadband data in order to reap the benefits of extra low‐ and high‐frequency contents. At the low end, as the sharp low‐cut decay gets closer to zero frequency, it becomes harder for a well tie to estimate the low‐frequency response correctly. The fundamental difficulty is that well logs are too short to allow accurate estimation of the long‐period content of the data. Three distinctive techniques, namely parametric constant phase, frequency‐domain least squares with multi‐tapering, and Bayesian time domain with broadband priors, are introduced in this paper to provide a robust solution to the wavelet estimation problem for broadband seismic data. Each of these techniques has a different mathematical foundation that would enable one to explore a wide range of solutions that could be used on a case‐by‐case basis depending on the problem at hand. A case study from the North West Shelf Australia is used to analyse the performance of the proposed techniques. Cross‐validation is proposed as a robust quality control measure for evaluating well‐tie applications. It is observed that when the seismic data are carefully processed, then the constant phase approach would likely offer a good solution. The frequency‐domain method does not assume a constant phase. This flexibility makes it prone to over‐fitting when the phase is approximately constant. Broadband priors for the time‐domain least‐squares method are found to perform well in defining low‐frequency side lobes to the wavelet.

Accuracy of wavelets, seismic inversion, and thin-bed resolution

“…White and Zabihi Naeini (2014) proposed a practical frequency domain approach to handle the low frequency decay of the wavelet; both on the amplitude and phase spectra. Following this work, Zabihi Naeini et al (2016) proposed new techniques for wavelet estimation for broadband seismic data namely parametric constant phase, frequency domain least-squares with multi-tapering and time domain Bayesian least-squares. Here we examine the parametric constant phase and time domain Bayesian least-squares and compare it to the traditionally derived wavelets using the Walden and White method (1998).…”

“…For full details of these techniques we refer to the above mentioned papers. Figure 1 shows top reservoir facies maps obtained by inverting the seismic using a) the standard reference wavelets (Walden and White, 1998), b) the constant phase wavelets, and c) the time domain Bayesian least-squares (Zabihi Naeini et al, 2016). Figure 2 shows the corresponding wavelets for the near angle stack where one can observe a smooth tail in wavelets using the new methods as well as a better spectral decay at the low frequency.…”

“…1) an Ormsby wavelet with high frequency deficiency, 4-8-25-55 Hz, 2) an Ormsby wavelet with low frequency deficiency, 5-9-30-60 Hz, 3) an estimated wavelet using broadband Bayesian time domain method (Zabihi Naeini et al, 2016). These wavelets are shown in Figure 6 and as can be observed one has to look closely to spot the differences.…”