Well tie for broadband seismic data |
| |
| Authors: | Ehsan Zabihi Naeini James Gunning Roy White |
| |
| Institution: | 1. Ikon Science ‐ R&D, Causeway House, The Causeway, Teddingon, London, United Kingdom of Great Britain and Northern Ireland;2. CSIRO Petroleum, Ian Wark Laboratory, Clayton, Victoria, Australia;3. Birkbeck, University of London, Walton on Thames, United Kingdom of Great Britain and Northern Ireland |
| |
| 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. |
| |
| Keywords: | well tie wavelet broadband seismic inversion |
|
|
