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A statistically driven spectral method was carried out on 3D seismic data and well logs in ‘’VIC’’ Field within the Niger Delta with the aim of deriving reservoir properties and delineating stratigraphic features using edge detection attributes like coherence so as to have a better and clearer view of subsurface structure of a reservoir interval that possesses hydrocarbon using Spectral method.
A suite of data consisting of seismic sections and composite logs comprising Gamma-ray, Resistivity, Spontaneous Potential, Sonic Time and Porosity logs (density and Neutron) were utilized to identify reservoir interval on log signature across wells 4 and 5 and the reservoir interval obtained was between 11,164 feet and 11,196 feet. Edge detection attribute like coherence was computed from the amplitude data in time domain and transformed to frequency domain using Fourier Transform tool in MATLAB. In order to display well log in time, well to seismic tie was carried out using check shot data which was used as time to depth relationship.
The analysis of the spectral domain shows distinct bright spots that vary with measured depth due to variation in fluid and formation properties. The results led to an enhancement of seismic data interpretation in the field of study due to a spectral technique method that was applied to calculate the frequency slices. The results indicate that the spectral domain in coherence attributes revealed better geological features and the reservoir character such as faults and fractures.
Frequency domain gives better geological maps as it is used to filter data, which means it is an enhancement of hidden features in time domain and gives a smoother variation of the features that has low frequency values. A reservoir with low frequency values is a sandy environment showing stratigraphy features. Hence, the reservoir is suspected to be a channel fill reservoir. This implies that Spectral domain (frequency) defines major geological areas of the ‘’VIC’’ field and gives much clearer image of the reservoir features within the field than in time domain.