On the seismic wavelet estimative and reflectivity recovering based on linear inversion: Well-to-seismic tie on a real data set from Viking Graben, North Sea

Abstract

Tying seismic data to well data is critical in reservoir characterization. In general, the main factors controlling a successful seismic well tie are an accurate time-depth relationship and a coherent wavelet estimate. Wavelet estimation methods are divided into two major groups: statistical and deterministic. Deterministic methods are based on using both the seismic trace and the well data to estimate the wavelet. Statistical methods use only the seismic trace and generally require assumptions about the wavelet's phase or assume a random process reflectivity series. We compare the estimation of the wavelet for seismic well tie purposes through least squares minimization and zero-order quadratic regularization with the results obtained from homomorphic deconvolution. Both methods do not make any assumptions regarding the wavelet’s phase or the reflectivity. The best-estimated wavelet was used as input to sparse-spike deconvolution to recover the reflectivity near the well location. The results show that the wavelets estimated from both deconvolutions are similar, which builds our confidence in their accuracy. The reflectivity of the seismic section was recovered according to known stratigraphic markers (from gamma-ray logs) present in the real dataset from the Viking Graben field, Norway.

The high‐resolution seismic deconvolution method based on joint sparse representation using logging–seismic data

“…In terms of deconvolution, there are mainly two types: linear deconvolution and nonlinear deconvolution (Sacchi et al, 1998). Linear deconvolution can improve the resolution by expanding the high-frequency components within the original frequency band of seismic data (Treitel & Lines, 1982; de Macedo & * E-mail: yaojun.wang@uestc.edu.cn de Figueiredo, 2020). Nonlinear deconvolution (Siewert et al, 2015), by adding various sparsity constraints, can predict high frequency beyond the original frequency band and improve the seismic data resolution greatly.…”