Fault Enhancement Using Spectrally Based Seismic Attributes -- Dewett and Henza SEG 2015

Fault Enhancement Using Spectrally Based Seismic AttributesDustin Dewett* and Alissa HenzaBHP BillitonSEG 2015

SummaryThe 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

Contents

Slide 3

Generalized Workflow

Case Study #1: Low noise

Case Study #2: High noise

Conclusions

Dewett and Henza, BHP Billiton, SEG 2015

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.


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

volumesfinal result


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

43 Hz

Step 2: Decompose Data Into Spectra

Slide 7







• Addition



37 Hz20 Hz


Step 3: Compute Seismic Attributes







• Addition



Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical Slide 8

Step 4: Optimize Dip and Continuity

Slide 9




Step 4: Optimize dip and continuity



• Addition




Step 5: Filter and Smooth Edges

Slide 10







• Addition




Step 6a: Combine Volumes Through Addition

Slide 11







• Addition




Step 6b: Optional Machine Learning

Slide 12







• Addition




Step 7: Optional Second Stage Dip Filter

Slide 13







• Addition




Contents

Slide 14




Conclusions


Case Study #1Fault Enhanced Similarity and Spectral Similarity

Slide 15Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical

Case Study #1Fault Enhanced Similarity and Spectral Similarity

Slide 16

Improved fault connectivity (yellow arrows) and more reasonable fault dip (red rectangle) in the Spectral Similarity Volume


Fault Interpretation Using Spectral Similarity

Slide 17courtesy of Craig Docherty

• Useful in manual fault

interpretation

• Computer based fault

extraction

• Fault QC and refinement


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)


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

Ma

gn

itu

de

Lo

wH

igh


Contents

Slide 20




Conclusions


Case Study #2Results of Structure Orientated Filtering


Case Study #2Similarity compared to Spectral Similarity


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

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.

Backup slide showing data range comparison

Dewett and Henza, BHP Billiton, SEG 2015 Data owned by Global Geophysical Slide 26

Science

Fault Enhancement Using Spectrally Based Seismic Attributes -- Dewett and Henza SEG 2015