Continuous Wavelet Transform Based Spectral Decomposition of 3d Seismic Data for Reservoir Characterization in Oyi Field, se Niger Delta
- 1 Geophysics Research Group, Department of Physics, University of Port Harcourt, P.O. Box 122, Choba, Port Harcourt, Nigeria
Abstract
Continuous Wavelet Transform (CWT) is one of the most used wavelet transform based spectral decomposition methods in the direct detection of hydrocarbons and as an indicator of stratigraphic variability due to its optimal time-frequency resolution and variable time window requirements. CWT based spectral decomposition was applied on a 3D Post Stack Time Migrated (PSTM) seismic data acquired over a producing field using morlet wavelet and a time varying analysis window. Frequency amplitude slices of 10 Hz to 50 Hz were extracted from a 1625 ms HD1 seismic horizon and analyzed for possible hydrocarbon indications and stratigraphy. Hydrocarbon filled reservoir sands were delineated as low frequency high amplitude spectral anomalies between 20 Hz to 30 Hz frequency band coincident with the locations of producing wells in the field. Also, high frequency high amplitude spectral anomaly were delineated at predominantly 50 Hz frequency indicating a thin-bed with probable hydrocarbon saturation particularly gas, suspected to be a bypass hydrocarbon zone. Result also shows amplitude diminution with increasing frequency attributed to local variations in lithology. Therefore, appropriate application of this method will improve reservoir characterization and enhanced bed thickness definition, de-risk prospects and minimize dry well drilling in the field.
DOI: https://doi.org/10.3844/ajassp.2017.766.771
Copyright: © 2017 Chukwuemeka Ngozi Ehirim and Andifon Sylvester Akpan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Continuous Wavelet Transform
- Spectral Decomposition
- Frequency Maps
- Spectral Anomalies