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Application of spectral decomposition seismic attribute for identification geological characteristics Mohammad Reza Saadati Nejad (1) , Hosein Hasani (1) , Mojtaba Mohammado Khorasani (2) , Abdolrahim Javaherian (3) and Mohammad Reza Sokooti (2)* (1) Faculty of Petroleum and Mining Engineering, Amirkabir University of Technology, Tehran, (2) National Iranian Oil Company, Exploration Directorate, Geophysics Dept., Tehran, (3) Institute of Geophysics, University of Tehran, P. O. Box 14155-6466, Tehran. [email protected], [email protected], [email protected], [email protected], [email protected] Summary Seismic interpretation in principal tries to find any small changes, as subtle stratigraphic plays and areas of low-relief faulting. Looking at the amplitude of reflections at particular frequencies may be helpful to see more clearly features which may not be seen from a fixed map view. Spectral decomposition breaks the seismic signal into its frequency components. This title refers to methods produces frequency spectrum of each sample of seismic trace. The method includes variety of algorithms, continues wavelet transform (CWT) and Fast Fourier transform (FFT) are most common in exploration. Although work is not the first attempt to translate attributes to, but an effort to visualize geological features in presence of reef structure in Sarvak, directly from geophysical data. The results from spectral decomposition display of 3D seismic data enable interpretation of seismic data very fast and effective in comparison with standard interpretation have done in previous works. Introduction Usually conventional seismic and their attributes are used to interpret the geophysical data to the geology. Data contains wide range of frequencies sometimes could hide a particular event that is trying to be detected. Spectral decomposition is a quick and effective method that gives better definition to determine stratigraphic architecture and structural features. Seismic interpretation in principal disciplines tries to find seismic geomorphology. To study any small changes, especially subtle stratigraphic plays or areas of low-relief faulting, in it 71 st EAGE Conference & Exhibition — Amsterdam, The Netherlands, 8 - 11 June 2009
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Page 1: Application of Spectral Decomposition Seismic Attribute For

Application of spectral decomposition seismic attribute for identification geological characteristics

Mohammad Reza Saadati Nejad(1), Hosein Hasani(1), Mojtaba Mohammado Khorasani(2), Abdolrahim Javaherian(3) and Mohammad Reza Sokooti(2)*

(1)Faculty of Petroleum and Mining Engineering, Amirkabir University of Technology, Tehran, (2)National Iranian Oil Company, Exploration Directorate,

Geophysics Dept., Tehran, (3)Institute of Geophysics, University of Tehran, P. O. Box 14155-6466, Tehran.

[email protected], [email protected], [email protected], [email protected], [email protected]

Summary Seismic interpretation in principal tries to find any small changes, as subtle stratigraphic plays and areas of low-relief faulting. Looking at the amplitude of reflections at particular frequencies may be helpful to see more clearly features which may not be seen from a fixed map view. Spectral decomposition breaks the seismic signal into its frequency components. This title refers to methods produces frequency spectrum of each sample of seismic trace. The method includes variety of algorithms, continues wavelet transform (CWT) and Fast Fourier transform (FFT) are most common in exploration.Although work is not the first attempt to translate attributes to, but an effort to visualize geological features in presence of reef structure in Sarvak, directly from geophysical data. The results from spectral decomposition display of 3D seismic data enable interpretation of seismic data very fast and effective in comparison with standard interpretation have done in previous works.

Introduction

Usually conventional seismic and their attributes are used to interpret the geophysical data to the geology. Data contains wide range of frequencies sometimes could hide a particular event that is trying to be detected. Spectral decomposition is a quick and effective method that gives better definition to determine stratigraphic architecture and structural features. Seismic interpretation in principal disciplines tries to find seismic geomorphology. To study any small changes, especially subtle stratigraphic plays or areas of low-relief faulting, in it may be helpful to look at the amplitude of reflections at particular frequencies. With this technique it is possible to analyze independently each frequency revealing features that were hidden before, to see more clearly features which may not be seen from a fixed map view.Since time-frequency mapping is a non-unique process, there exist various time-frequency analysis methods. The first and widely used method is the short-time Fourier transform (STFT) in which a time-frequency spectrum is produced by taking the Fourier transform over a short time window (Cohen, 1995). The continuous wavelet transform (CWT) provides represents the frequency band from scaled wavelet, has an advantage as does not relate to a fix window. The target carbonate formation has with two major fancies, a massive limestone containing Rudists the other is deeper water fancies of thinner bedded limestone. According to analogy reefs occur in the first layer in the area, but absence of wells data is a problem to reach the formation directly. This paper tries to evaluate geophysical this potential.

Methodology

The study is performed on 3D seismic data; the wells did not touch the target formation in the interested area (Figure1). The seismic data was not high frequency and post stack band limited filtering and scaling was performed in processing sequence. Therefore data was not ideal for such studies. In order to evaluate presence of this reef according to temporal thickness, both FFT and CWT were applied to data as each one has advantages. The Time

71st EAGE Conference & Exhibition — Amsterdam, The Netherlands, 8 - 11 June 2009

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horizons, picked with some guide lines then auto tracked to whole area. The result horizon then smoothed in order to prevent and misreading amplitude in cube. The FFT method, which is a window base method, is the classical way to extract and evaluate frequency spectrum of data in seismic data processing, when experts interested in data from a long window. To concentrate on the target area (e.g. a faulted horizon slice) we need decreasing the window length, the frequency resolution in frequency domain will be compromised. The continuous wavelet transform (CWT) provides a different approach to time-frequency analysis. It produces a time-scale map. Since scale represents a frequency band, it is not intuitive if we wish to interpret the frequency content of the signal; the advantage is calculation does not need any window of data.The isopatch map of Mishrif in Figure1 displays a build up, that show the formation has a reef probable structure as thickness tends to be zero out above the structure and in Figure2 show onlap on reef structure with Gamma-ray log. The RMS amplitude of Mishrif horizon slice (Figure3) shows a ring pattern around the paleo-high. Relief around the horizon shows a thickness of the formation is in creases in the direction.

Figure 1: Left) Isopatch map. Right) similarity attribute of Mishrif surface and wells location. Red color in left shows thinning and blue color show thickness of the formation.

Sarvak, the target formation is lateral equivalent of Maudud, Ahamdi-Wara and Rumalia-Mishrif of Kuwait. It developed into two facies a massive limestone deposited in a neretic environment containing Rudists Gastropods and Pelecypods, the other is deep water limestone. The Kazhdomi formation as source rock could make a good hydrocarbon system. The upper contact of Sarvak is marked by the erosional unconformity. The isopatch map of Mishrif Figure1 displays the build up. Seismic cross section shows a buildup in middle Figure2, the Saravk is thinner in direction of buildup and disappear in presence of unconformity. At the Sarvak horizon, the spectral amplitude maps were generated around a 10 to 80 ms time window. Within this window the total spectral amplitude were extracted.

Figure 2: Seismic cross-section from Sarvak formation and above and under formation with Gamma-ray log. Pink, green, yellow, red, blue and pink lines are top of Illam, Mishrif,

71st EAGE Conference & Exhibition — Amsterdam, The Netherlands, 8 - 11 June 2009

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Ahmadi, Maudud, Kazhdomi and Burgan formation consequently. Red arrows indicate suggestive system track in Mishrif member too.

After parameter test on horizon base attribute, we compare a brief of our result in Figure3. The original RMS amplitude of 8ms above and 16ms below the interpreted Sarvak horizon 3a and corresponding amplitude of 15Hz and 28Hz frequency shows the direct related of peak spectral frequency in horizon slices.

Figure 3: The result of RMS amplitude of broad band horizon slice of upper Sarvak. Left) the thicker thickness event at the 7 Hz common frequency horizon slice. Middle) the mid thickness the 15Hz common frequency horizon slices. Right) the thinner the 30 Hz amplitude of upper Sarvak. There is a better show of reef boundary.

(a)

(b)

71st EAGE Conference & Exhibition — Amsterdam, The Netherlands, 8 - 11 June 2009

HighAmp.

LowAmp.

HighAmp.

LowAmp.

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On the seismic cross section the result of CWT with Morelet wavelet shows a low resolution results so in the reef boundary interpretation is not exact. The reason is could be relate to the nature of Morlete wavelet in presence of sidelobs. Layer shows stronger amplitude strength in thicker part of reef and weaker in higher frequency. There is existing well data id not enough to calibrate the result. A 25 Hz seismic section of Figure4 obtained using CWT with Morlet wavelet, method shows an anomaly pattern that shows it come from tuning effect of Mishrif thinning over the old Sarvak basement.

Figure 4: Representation of spectral decomposition results in Sarvak formation. White, blue and black arrows are representation spectral anomalies in base of Mishrif and pink and brown arrows are representation spectral anomalies in base of Maudud.

After some test, FFT shows better resolution on cross sections where we interpreted the possible reef. Figure5 is a comparison seismic data flatted on Ahamdi, in right, and the attribute of peak amplitude of 10Hz in left. After this step the reinterpreted horizon for top of reef were reinterpreted.

Figure 5: Left) FFT-10Hz. Right) seismic section flattened on Ahmadi. Green line is new interpretation result, and blue line is our old interpretation according to original seismic data

Animating trough the frequency slices and using an adequate color bar we obtained around 10 Hz the best definition of the carbonated bodies (Figure6).

71st EAGE Conference & Exhibition — Amsterdam, The Netherlands, 8 - 11 June 2009

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Figure 6: The figure of reef structure in timeslice with FFT method. Blue line is our old interpretation according to seismic data and green arrows are reef location.

Conclusions

Spectral decomposition study was performed on a data set that was originally processed for structural interpretation. The method helps us to understand the distribution the reef boundary much better than original seismic data. It proved to be efficient even in carbonated reservoirs allowing individualize the geological bodies that were not easily defined in amplitude maps or attributes depending of it.

Acknowledgement

The authors thank Geophysics department of NIOC exploration directorate for permission to publish this paper.

References

Castagna, J.P. and Sun, S. [2006] Comparison of spectral decomposition methods. First Break, 24, 75–79.Castagna, J.P., Sun, S. and Siegfried, R.W. [2003] Instantaneous spectral analysis: Detection of low-frequency shadows associated with hydrocarbons. The Leading Edge, 22, 120–127.Deng, J.X., Han, D.H., Liu, J. and Yao, Q. [2007] Application of spectral decomposition to detect deepwater gas reservoir. SEG Technical Program, Expanded Abstracts.Liu, J. and Marfurt, K.J. [2007] Instantaneous spectral attributes to detect channels. Geophysics, 72(2), 23–31.Partyka, G., Gridley, J. and Lopez, J. [1999] Interpretational applications of spectral decomposition in reservoir characterization. The Leading Edge, 18, 353–360.

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