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Estimation of the seismic wavelet through homomorphic deconvolution and well log data: application on well‐to‐seismic tie procedure
Author(s) -
de Macedo Isadora A.S.,
de Figueiredo Jose Jadsom S.,
de Sousa Matias C.,
Nascimento Murillo J.S.
Publication year - 2020
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1111/1365-2478.12908
Subject(s) - wavelet , deconvolution , computer science , algorithm , seismic inversion , geology , artificial intelligence , mathematics , geometry , azimuth
Wavelet estimation and well‐tie procedures are important tasks 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 homomorphic deconvolution, however, does not take these premises into account. In this work, we propose an approach to estimate the seismic wavelet using the advantages of the homomorphic deconvolution and the deterministic estimation of the wavelet, which uses both seismic and well log data. The feasibility of this approach is verified on well‐to‐seismic tie from a real data set from Viking Graben Field, North Sea, Norway. The results show that the wavelet estimated through this methodology produced a higher quality well tie when compared to methods of estimation of the wavelet that consider the classical assumptions of the convolutional model.