Wavelet Estimation for Broadband Seismic Data

Abstract

The volumes of broadband seismic data acquired and processed by the industry have grown rapidly. There is also an increasing emphasis on the benefits of broadband seismic for quantitative interpretation. The bottleneck for achieving a satisfactory quantitative interpretation and subsequently reservoir parameter estimation is the well tie, a process through which the seismic wavelet is estimated. However, broadband seismic data pose a challenge for well ties as the duration of the well log is often inadequate to estimate the low frequency decay towards zero frequency. 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. A case study from North West Shelf Australia is used to analyse the performance the proposed techniques. Generally, when the seismic data is carefully processed then the constant phase approach would likely offer a good solution. Broadband priors for the time domain least-squares method are found to perform well in defining low-frequency side-lobes to the wavelet.