Fault interpretation in seismic data is a critical task that must be completed to thoroughly understand the structural history of the subsurface. The development of similarity-based attributes has allowed geoscientists to effectively filter a seismic data set to highlight discontinuities that are often associated with fault systems. Furthermore, there are numerous workflows that provide, to varying degrees, the ability to enhance this seismic attribute family. We have developed a new method, spectral similarity, to improve the similarity enhancement by integrating spectral decomposition, swarm intelligence, magnitude filtering, and orientated smoothing. In addition, the spectral similarity method has the ability to take any seismic attribute (e.g., similarity, curvature, total energy, coherent energy gradient, reflector rotation, etc.), combine it with the benefits of spectral decomposition, and create an accurate enhancement to similarity attributes. The final result is an increase in the quality of the similarity enhancement over previously used methods, and it can be computed entirely in commercial software packages. Specifically, the spectral similarity method provides a more realistic fault dip, reduction of noise, and removal of the discontinuous “stair-step” pattern common to similarity volumes.

1. Fault Enhancement Using
Spectrally Based Seismic
Attributes
Dustin Dewett* and Alissa Henza
BHP Billiton
SEG 2015

2. Summary
The Spectral Similarity has quickly become the standard for fault attributes at BHP Billiton
Slide 2
• We have developed a new attribute method that better identifies potential fault planes in both low and high
noise data.
• The Spectral Similarity has been favored by 20+ experienced geoscientists (15-20 year average) with
structural geology training and/or over a decade of seismic interpretation experience at BHP Billiton and
our partners.
Images are courtesy of
Craig DochertyDewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

3. Contents
Slide 3
Generalized Workflow
Case Study #1: Low noise
Case Study #2: High noise
Conclusions
Dewett and Henza, BHP Billiton, SEG 2015

4. Definitions
Slide 4
• Similarity: a family of edge-detection attributes that includes coherence, variance, Sobel filter,
etc.
• Swarm intelligence: a family of algorithms that uses decentralized self-organization to
perform a task (examples include particle swarm optimization, ant colony optimization, or
differential evolution).
• Machine learning: a sub-discipline of computer science that consists of algorithms that can
learn from and make predictions on data (examples include artificial neural networks, self-
organized maps, k-means clustering).
• Fault Enhanced similarity: refers to the patented filtering process for similarity enhancement
described by Dorn et al. (2012).
• Spectral similarity: refers to the workflow described in this paper for similarity enhancement.
Dewett and Henza, BHP Billiton, SEG 2015

5. Simplified Spectral Similarity Workflow
Slide 5
filter seismic spectral
decomposition
pick spec-d
volumes
run seismic
attributes
optimize dip
response
filter and
smooth edges
combine
volumes
final result
Dewett and Henza, BHP Billiton, SEG 2015

6. Step 1: Filter Amplitude As Needed
Slide 6
Step 1: Filter data as needed
Step 2: Decompose data into spectra
Step 3: Compute seismic attributes
Step 4: Optimize dip response
Step 5: Filter and smooth edge optimized volume
Step 6: Combine volumes
• Addition
• Machine Learning
Step 7: Dip filter faults
Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

7. 43 Hz
Step 2: Decompose Data Into Spectra
Slide 7
Step 1: Filter data as needed
Step 2: Decompose data into spectra
Step 3: Compute seismic attributes
Step 4: Optimize dip response
Step 5: Filter and smooth edge optimized volume
Step 6: Combine volumes
• Addition
• Machine Learning
Step 7: Dip filter faults
37 Hz
20 Hz
Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

8. Step 3: Compute Seismic Attributes
Step 1: Filter data as needed
Step 2: Decompose data into spectra
Step 3: Compute seismic attributes
Step 4: Optimize dip response
Step 5: Filter and smooth edge optimized volume
Step 6: Combine volumes
• Addition
• Machine Learning
Step 7: Dip filter faults
Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical Slide 8

9. Step 4: Optimize Dip and Continuity
Slide 9
Step 1: Filter data as needed
Step 2: Decompose data into spectra
Step 3: Compute seismic attributes
Step 4: Optimize dip and continuity
Step 5: Filter and smooth edge optimized volume
Step 6: Combine volumes
• Addition
• Machine Learning
Step 7: Dip filter faults
Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

10. Step 5: Filter and Smooth Edges
Slide 10
Step 1: Filter data as needed
Step 2: Decompose data into spectra
Step 3: Compute seismic attributes
Step 4: Optimize dip and continuity
Step 5: Filter and smooth edge optimized volume
Step 6: Combine volumes
• Addition
• Machine Learning
Step 7: Dip filter faults
Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

11. Step 6a: Combine Volumes Through Addition
Slide 11
Step 1: Filter data as needed
Step 2: Decompose data into spectra
Step 3: Compute seismic attributes
Step 4: Optimize dip and continuity
Step 5: Filter and smooth edge optimized volume
Step 6: Combine volumes
• Addition
• Machine Learning
Step 7: Dip filter faults
Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

12. Step 6b: Optional Machine Learning
Slide 12
Step 1: Filter data as needed
Step 2: Decompose data into spectra
Step 3: Compute seismic attributes
Step 4: Optimize dip and continuity
Step 5: Filter and smooth edge optimized volume
Step 6: Combine volumes
• Addition
• Machine Learning
Step 7: Dip filter faults
Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

13. Step 7: Optional Second Stage Dip Filter
Slide 13
Step 1: Filter data as needed
Step 2: Decompose data into spectra
Step 3: Compute seismic attributes
Step 4: Optimize dip and continuity
Step 5: Filter and smooth edge optimized volume
Step 6: Combine volumes
• Addition
• Machine Learning
Step 7: Dip filter faults
Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

14. Contents
Slide 14
Generalized Workflow
Case Study #1: Low noise
Case Study #2: High noise
Conclusions
Dewett and Henza, BHP Billiton, SEG 2015

15. Case Study #1
Fault Enhanced Similarity and Spectral Similarity
Slide 15Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

16. Case Study #1
Fault Enhanced Similarity and Spectral Similarity
Slide 16
Improved fault connectivity (yellow arrows) and more reasonable fault dip (red rectangle) in the Spectral Similarity Volume
Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

17. Fault Interpretation Using Spectral Similarity
Slide 17courtesy of Craig Docherty
• Useful in manual fault
interpretation
• Computer based fault
extraction
• Fault QC and refinement
Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

18. Automatic Fault Interpretation Using Spectral Similarity
Slide 18
• 332 faults total
• 33% (108 faults) require no edits (orange)
• 63% (209 faults) require basic fault splitting
(blue)
• 4% (15 faults) mesh edits only (red)
Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

19. Spectral Similarity with Peak Frequency
Slide 19
Spectral Similarity communicates structural information, while peak frequency communicates lithological and
stratigraphic information. This yields a more complete geologic understanding.
Frequency
Low High
Magnitude
LowHigh
Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

20. Contents
Slide 20
Generalized Workflow
Case Study #1: Low noise
Case Study #2: High noise
Conclusions
Dewett and Henza, BHP Billiton, SEG 2015

21. Case Study #2
Results of Structure Orientated Filtering
Slide 21Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

22. Case Study #2
Similarity compared to Spectral Similarity
Slide 22Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

23. Summary
Slide 23
Spectral Similarity…
• is the preferred fault enhancement method at BHP Billiton,
• integrates any seismic attribute and spectral decomposition,
• will improve computer based and human driven interpretation workflows, and
• will enhance both small faults and large faults.
Right hand images are courtesy of
Craig DochertyDewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

25. Selected References
Dewett and Henza, BHP Billiton, SEG 2015 Slide 25
Al-Dossary, S., and K. Al-Garni, 2013, Fault detection and characterization using a 3D multidirectional Sobel filter: Saudi Arabia Section Technical Symposium and Exhibition, Society
of Petroleum Engineers, SPE-168061-MS.
Aqrawi, A. and T. Boe, 2011, Improved fault segmentation using a dip guided and modified 3D Sobel filter. SEG Technical Program Expanded Abstracts 2011: pp. 999-1003
Basir H., A. Javaherian, and M. Yaraki, 2013, Multi-attribute ant-tracking and neural network for fault detection: a case study of an Iranian oilfield: J. Geophys. Eng. 10, 01509.
Chopra, S. and K. J. Marfurt, 2010, Seismic attributes for prospect identification and reservoir characterization: SEG.
Dorn, G., B. Kadlec, and M. Patty, 2012, Imaging faults in 3D seismic volumes: Presented at the 82nd Annual International Meeting, SEG.
Gao, D., 2013, Wavelet spectral probe for seismic structure interpretation and fracture characterization: A workflow with case studies: Geophysics 78, O57-O67.
Garsztenkorn A. and K. J. Marfurt, 1999, Eigenstructure-based coherence computations as an aid to 3-D structural and stratigraphic mapping: Geophysics 64, P1468-9.
Marfurt, K. J., 2006, Robust estimates of reflector dip and azimuth: Geophysics, 71, 29–40.
Marfurt, K. J., R. Kirlin, S. Farmer, and M. Bahorich, 1998, 3-D seismic attributes using a semblance-based coherency algorithm: Geophysics 63, P1150-65.
Pedersen, S., T. Randen, L. Sønneland, and O. Steen, 2002, Automatic 3D fault interpretation by artificial ants: Presented at the 72nd Annual International Meeting, SEG.
Randen, T., S. Pedersen, and L. Sønneland 2001, Automatic extraction of fault surfaces from three‐dimensional seismic data. SEG Technical Program Expanded Abstracts 2001: pp.
551-554 doi: 10.1190/1.1816675.

26. Backup slide showing data range comparison
Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical Slide 26