Seismic channel edge detection using 3D shearlets-a study on synthetic and real channelised 3D seismic data

In today’s industry, automatic detection of geologic features such as faults and channels is a challenging problem when the quality of data is not good. Edge detection filters are generally applied for the purpose of locating such features. Until now, edge detection has been carried out on rectangularly sampled 3D seismic data. The computational cost of edge detection can be reduced by exploring other sampling approaches instead of the regular rectangular sampling commonly used. Hexagonal sampling is an alternative to rectangular sampling that requires 13.4% less samples for the same level of accuracy. The hexagonal approach is an efficient method of sampling with greater symmetry compared with the rectangular approach. Spiral architecture can be used to handle the hexagonally sampled seismic data. Spiral architecture is an attractive scheme for handling 2D images that enables processing 2D data as 1D data in addition to the inherent hexagonal sampling advantages. Thus, the savings in number of samples, greater symmetry, and efficient data handling capability makes hexagonal sampling an ideal choice for computationally exhaustive operations. For the first time to our knowledge, we have made an attempt to detect edges in hexagonally sampled seismic data using spiral architecture. We compared edge detection on rectangular and hexagonally sampled seismic data using 2D and 3D filters in rectangular and hexagonal domains. We determined that hexagonal processing results in exceptional computational savings, when compared with its rectangular processing counterpart.

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Edge detection and stratigraphic analysis using 3D seismic data

10.1190/1.1826632 ◽

1996 ◽

Cited By ~ 33

Author(s):

Y. Luo ◽

W. G. Higgs ◽

W. S. Kowalik

Keyword(s):

Edge Detection ◽

Seismic Data ◽

3D Seismic ◽

Stratigraphic Analysis ◽

3D Seismic Data

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An edge detection algorithm of 3D seismic data based on interval two-dimensional Hilbert transform

10.1190/igc2018-298 ◽

2018 ◽

Author(s):

Bingnan Lv ◽

Xuehua Chen ◽

Jiong Li ◽

Lei Qu ◽

Xin Luo

Keyword(s):

Edge Detection ◽

Hilbert Transform ◽

Seismic Data ◽

Detection Algorithm ◽

Two Dimensional ◽

3D Seismic ◽

Edge Detection Algorithm ◽

3D Seismic Data

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A Statistically Driven Spectral Method for Deriving Reservoir Properties Using 3D Seismic Data and Well Log Suites

Asian Journal of Research and Reviews in Physics ◽

10.9734/ajr2p/2018/v1i124588 ◽

2018 ◽

pp. 1-15

Author(s):

V. B. Olaseni ◽

Y. S. Onifade ◽

J. O. Airen ◽

L. Adeoti

Keyword(s):

Edge Detection ◽

Spectral Method ◽

Time Domain ◽

Seismic Data ◽

Low Frequency ◽

Spectral Domain ◽

Well Log ◽

3D Seismic ◽

Reservoir Properties ◽

3D Seismic Data

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.

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