From the academic to the oil industry, it is known that wellto-seismic-tie is an important step in seismic processing and interpretation. Deconvolutional statistical methods to estimate the proper wavelet, in general, are based on the assumptions of the classical convolutional model, which implies a random process reflectivity and a minimum-phase wavelet. The estimative of the wavelet for well-to-seismic-tie purposes through least squares minimization with zero order quadratic regularization is compared with the results obtained from the deterministic 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 the sparse-spike deconvolution to recover the reflectivity near to the well location. The results show that the wavelets estimated from both deconvolutions are similar, which suggests their feasibility. 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.
Presentation Date: Wednesday, September 18, 2019
Session Start Time: 1:50 PM
Presentation Time: 3:05 PM
Location: Poster Station 4
Presentation Type: Poster
