PowerPoint Presentation

The State and Future of Multispectral Fault EnhancementDustin T. DewettBHP Billiton, Deepwater and Shelf Reservoirs, 2017

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Summary

Slide 2Similarity attributes have proven themselves to be invaluable for modern seismic interpretation,Numerous techniques exist to enhance those attributes to gain even more information from the data,And various post-stack (primarily), interpreter driven filters allow one to better precondition the seismic data.

Spectral decomposition techniques reveal subtleties in data that would otherwise remain hidden,By using frequency dependent operations, we can gain even better results from our dataSeveral researchers are currently working on exploiting these points to provide useful and easy workflows

Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017(Dewett and Henza, 2016)

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ContentsSlide 3Brief History of Multispectral Fault EnhancementSpectral Similarity WorkflowA Glimpse into the Future of Multispectral Fault EnhancementConclusionsDewett and Henza, BHP Billiton, SEG 2015

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Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017Slide 4(after Partyka et al., 1999)HistoryLooking at the 16 Hz (left) phase map as compared to the conventional response phase (right)

Faults appear more clearly defined and precise in the spectral decomposition phase mapRandom noise is minimized at certain frequencies (increased SNR at specific frequencies)

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Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017

RGB blending is a powerful tool that leverages spectral decomposition for the visual identification of faults,

Numerous potential methods exist that allow for the extraction of RGB based discontinuities

Inherently, RGB methods limit one to three spectral decomposition (typically magnitude) volumesSlide 5(after Henderson et al., 2008)

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Slide 6

(after Hardage, 2009)Looking at conventional seismic (left) compared to image produced from filter bank (8 to 16 Hz)

The frequencies that produce the optimal image in target and dataset dependentHigher frequencies allow for more precision and small fault throw detectionHigher frequencies are commonly more contaminated by noiseDewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017

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ContentsSlide 7Brief History of Multispectral Fault EnhancementSpectral Similarity WorkflowA Glimpse into the Future of Multispectral Fault EnhancementConclusions

Dewett and Henza, BHP Billiton, SEG 2015

Simplified Spectral Similarity WorkflowSlide 8Filter seismicSpectral decompositionIdentify optimal frequenciesRun seismic attributesSkeletonize lineamentsFilter lineaments and smooth as neededCombine volumesFinal resultDewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017

Slide 9

Step 1: Filter amplitude as needed

Step 1: Filter data as needed

Step 2: Decompose data into spectra

Step 3: Compute seismic attributes

Step 4: Skeletonize lineaments

Step 5: Filter and smooth lineaments

Step 6: Combine volumes

AdditionMachine Learning

Step 7: Mask faults on strike or dip

Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017(modified from Dewett and Henza, 2016)

Step 2: Decompose data into spectraSlide 10

43 HzStep 1: Filter data as needed

Step 2: Decompose data into spectra

Step 3: Compute seismic attributes

Step 4: Skeletonize lineaments

Step 5: Filter and smooth lineaments

Step 6: Combine volumes

AdditionMachine Learning

Step 7: Mask faults on strike or dip

37 Hz

20 Hz(modified from Dewett and Henza, 2016)Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017

Step 3: Compute seismic attributesStep 1: Filter data as needed

Step 2: Decompose data into spectra

Step 3: Compute seismic attributes

Step 4: Skeletonize lineaments

Step 5: Filter and smooth lineaments

Step 6: Combine volumes

AdditionMachine Learning

Step 7: Mask faults on strike or dip

Slide 11(modified from Dewett and Henza, 2016)Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017

Step 4: Optimize dip and continuitySlide 12

Step 1: Filter data as needed

Step 2: Decompose data into spectra

Step 3: Compute seismic attributes

Step 4: Skeletonize lineaments

Step 5: Filter and smooth lineaments

Step 6: Combine volumes

AdditionMachine Learning

Step 7: Mask faults on strike or dip

(modified from Dewett and Henza, 2016)Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017

Step 1: Filter data as needed

Step 2: Decompose data into spectra

Step 3: Compute seismic attributes

Step 4: Skeletonize lineaments

Step 5: Filter and smooth lineaments

Step 6: Combine volumes

AdditionMachine Learning

Step 7: Mask faults on strike or dipStep 4: Optimize dip and continuitySlide 13

(modified from Dewett and Henza, 2016)Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017

Step 1: Filter data as needed

Step 2: Decompose data into spectra

Step 3: Compute seismic attributes

Step 4: Skeletonize lineaments

Step 5: Filter and smooth lineaments

Step 6: Combine volumes

AdditionMachine Learning

Step 7: Mask faults on strike or dipStep 6a: Combine volumes through additionSlide 14

(modified from Dewett and Henza, 2016)Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017

Step 1: Filter data as needed

Step 2: Decompose data into spectra

Step 3: Compute seismic attributes

Step 4: Skeletonize lineaments

Step 5: Filter and smooth lineaments

Step 6: Combine volumes

AdditionMachine Learning

Step 7: Mask faults on strike or dipStep 6b: Optional machine learningSlide 15

(modified from Dewett and Henza, 2016)Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017

Step 1: Filter data as needed

Step 2: Decompose data into spectra

Step 3: Compute seismic attributes

Step 4: Skeletonize lineaments

Step 5: Filter and smooth lineaments

Step 6: Combine volumes

AdditionMachine Learning

Step 7: Mask faults on strike or dipStep 7: Optional dip filterSlide 16

(modified from Dewett and Henza, 2016)Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017

Comparison #1Fault Enhanced Similarity and Spectral SimilaritySlide 17

(modified from Dewett and Henza, 2016)Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017

Slide 18

(modified from Dewett and Henza, 2016)Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017Comparison #1Fault Enhanced Similarity and Spectral Similarity

Fault interpretation using Spectral SimilaritySlide 19

Useful in manual fault interpretationComputer based fault extractionFault QC and refinement

(modified from Dewett and Henza, 2016)Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017

Automatic fault interpretation using Spectral SimilaritySlide 20

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)

(modified from Dewett and Henza, 2016)Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017

Spectral Similarity with Peak FrequencySlide 21Spectral Similarity communicates structural information, while peak frequency communicates lithological and stratigraphic information. This yields a more complete geologic understanding.

FrequencyLowHighMagnitudeLowHigh(modified from Dewett and Henza, 2016)Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017

ContentsSlide 22Brief History of Multispectral Fault EnhancementSpectral Similarity WorkflowA Glimpse into the Future of Multispectral Fault EnhancementConclusions

Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017

A Glimpse into the FutureSlide 23Increase in the number of workflows for multispectral fault enhancement by research groups and vendorsComparisons between broad-band or full-stack similarities and multispectral methodsExpect more papers on this and related topics as we gain a more complete understanding of various aspectsDewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017

A Glimpse into the FutureSlide 24(modified from Sui et al., 2015)Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017Comparison between 4 different spectral similarity attributes.

Li et al. (2014) based similarity using 23 Hz component,Sui et al. (2015) based spectral amplitude similarity algorithm,Modulus of 23 Hz complex spectral component based similarity,Gersztenkorn et al. (1999) eigenstructure based similarity(a)(b)(c)(d)

Multispectral Similarity and Fault EnhancementSlide 25is a rapidly developing area of active research

can integrate any seismic attribute and spectral decomposition,

will improve computer based and human driven interpretation workflows, and

will enhance both small faults and large faults (multiscale response).

Dewett, BHP Billiton, Deepwater and Shelf Reservoirs, 2017(modified from Dewett and Henza, 2016)

ReferencesDewett, D. T. and Hensa, A. A. [2015] Spectral similarity fault enhancement: Interpretation, 4, SB149-SB159.

Hardage, B. [2009] Frequencies are fault finding factors: Looking low aids data interpretation: AAPG Explorer, 30, no 9, 34.

Henderson, J., S. J. Purves, G. Fisher, and C. Leppard. [2008] Delineation of geological elements from RGB color blending of seismic attribute volumes: The Leading Edge, 27, 342350.

Sui, J., Zheng, X., and Li, Y. [2015] A seismic coherency method using spectral amplitudes: Applied Geophysics, 12, 356361.