Estimation of the seismic wavelet through homomorphic deconvolution and well log data: application on well-to-seismic tie procedure |
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| Authors: | Isadora AS de Macedo Jose Jadsom S de Figueiredo Matias C de Sousa Murillo JS Nascimento |
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| Institution: | 1. Faculty of Geophysics, Petrophysics and Rock Physics Laboratory - Prof. Dr. Om Prakash Verma, Federal University of Pará, Belém, Pará, Brazil
National Institute of Petroleum Geophysics (INCT-GP), Salvador, Bahia, Brazil;2. Faculty of Geophysics, Petrophysics and Rock Physics Laboratory - Prof. Dr. Om Prakash Verma, Federal University of Pará, Belém, Pará, Brazil |
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| Abstract: | 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. |
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| Keywords: | Homomorphic deconvolution Well-to-seismic-tie Seismic wavelet estimation convolutional model |
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