OpendTect is an attribute-study software, which is used in exploration seismology. This software is developed by dGB Earth Science group. In this presentation file, the OpendTect 5 is used to extract the fault system of F3_Demo seismic data, North sea, Netherlands.

1. OpendTect project
2015
Department of Petroleum
Engineering
Amirkabir University of Technology
(Tehran Polytechnic)
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2. Table of contents
 Generating a filter for:
 Random noise attenuation
 And to sharpen the faults
 Generating and plotting attributes
 The most positive curvature, The most negative curvature, Maximum
curvature, Minimum curvature, Apparent dip (45˚), Inline dip, Semblance,
Similarity, Instantaneous phase
 Conclusions
 Reference
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3. Question (1)
(a) Generate a filter for removing random noise and to sharpen the faults. This filter may be
combined byboth a “median filter” and “diffusion filter” with appropriate steering cube and
parameters.
(b) Plot the filtered seismic data and the original seismic data and compare them.
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4. The data with 100-350 inline range and 290-600 crossline range have been chosen.
Fig.1. original seismic data inline 200.
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5. Creating the steering cube
Bring up the Create Steering Seismics window via Processing > Dip
Steering > 3D > Create.
Fig.2. Detailed-dip-steered seismic section, 200 inline.
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6. Applying parameters for random noise attenuation
and fault enhancement
Fig.3. Similarity attribute setting.
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7. Fig.4. Detailed-steered similarity attribute applied to the
seismic section, inline 200. 7/37

8. To apply a dip-steered median filter to a seismic data set, you need to define an
attribute of type “Volume statistics”:
1. Start the attribute engine.
2. Select Volume Statistics as attribute type and apply the settings as below.
Fig.5. Detailed-dip-steered median filter setting.
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9. Fig.6. Detailed-dip-steered median filter applied to seismic
section, inline 200. 9/37

10. Dip-Steered Diffusion Filter
The dip-steered diffusion filter is used to replace low quality traces by neighbouring traces of better
quality. This migration will be performed using a Similarity attribute.
To apply a dip-steered diffusion filter to a seismic data use the defined similarity and dip-steered
median filter attributes.
For applying dip-steered median filter Start the attribute engine and Now specify Position as attribute
type.
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11. Fig.7. Detailed-dip-steered diffusion filter setting.
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12. Fig.8.Detailed-dip-steered diffusion filter applied to seismic
section, inline 200. 12/37

13. Fault Enhancement Filter
The fault enhancement filter is a combination of dip-steered median filter and diffusion
filter, modifying the seismic volume to enhance fault visibility. Based on similarity, the
data is smoothed away from the faults and sharpened at the fault location.
For applying fault enhancement filter start the attribute engine and now specify
mathematics as attribute type.
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14. Fig.9. Fault enhancement filter setting.
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15. Fig.10. Fault enhancement filter applied to seismic section with
12 msec time gate for similarity attribute, inline 200. 15/37

16. Fig.11. Original seismic section, inline 200.
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17. Fig.12. Fault enhancement filter applied to seismic section with
48 msec time gate for similarity attribute, inline 200. 17/37

18. After creating the new survey and importing data
Fig.13. Original seismic section, inline 200.
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19. Generate and plot the following attributes. Compare the results of following attributes on the
filtered data and on the original data:
– The most positive curvature
– The most negative curvature
– Maximum curvature
– Minimum curvature
– Apparent dip (450)
– Inline dip
– Semblance
– Similarity
– Instantaneous phase
Question (2)
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20. Figure.14. The most positive curvature applied to original seismic data, inline 200.
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21. Figure.15. The most positive curvature applied to filtered seismic data, inline 200.
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22. Figure.16. The most negative curvature applied to original seismic data, inline 200.
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23. Figure.17. The most negative curvature applied to filtered seismic data, inline 200.
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24. Figure.18. The maximum curvature applied to original seismic data, inline 200.
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25. Figure.19. The maximum curvature applied to filtered seismic data, inline 200.
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26. Figure.20. The minimum curvature applied to original seismic data, inline 200.
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27. Figure.21. The minimum curvature applied to filtered seismic data, inline 200.
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28. Figure.22. The apparent dip (45˚) applied to original seismic data, inline 200.
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29. Figure.23. The inline dip applied to original seismic data, inline 200.
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30. Figure.24. The detailed-dip-steered semblance attribute applied to original seismic data, inline
200. 30/37

31. Figure.25. The detailed-dip-steered semblance attribute applied to filtered seismic data, inline
200. 31/37

32. Figure.26. The detailed-dip-steered similarity attribute applied to original seismic data, inline
200. 32/37

33. Figure.27. The detailed-dip-steered similarity attribute applied to filtered seismic data, inline
200. 33/37

34. Figure.28. The instantaneous phase attribute applied to original seismic data, inline 200.
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35. Figure.29. The instantaneous phase attribute applied to filtered seismic data, inline 200.
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36. Conclusions
 Median filter attenuates the random noise.
 Diffusion filter sharpen the faults and other events exist in the seismic data.
 Fault enhancement filter attenuates the random noise where the similarity is more then
0.5 and sharpen the faults and discontinuity where the similarity is less than 0.5.
 Faults and fractures and their geometries can be determined from curvature attributes.
 Similarity and semblance attributes are both used in faults and fractures determination,
but as it has been showed similarity attribute sharpened the faults more than the
semblance attribute.
 Smaller time gate in similarity attribute will results more detail and then improve the
diffusion and the fault enhancement filter.
 The faults and fractures can be determined from sudden alteration in dip.
 If the instantaneous phase attribute is applied to filtered data, the random noise will be
attenuated in comparison with the time that it is applied to the original data.
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