5. Lateral Changes in Amplitude and Pattern Recognition
Society of Exploration Geophysicists and European Association of Geoscientists and Engineers eBooks · 2007
PreviousNext No AccessSeismic Attributes for Prospect Identification and Reservoir Characterization5. Lateral Changes in Amplitude and Pattern RecognitionAuthors: A. A. AdeogbaT. R. McHargueS. A. GrahamC. H. BlumentrittE. C. SullivanK. J. MarfurtS. ChopraV. AlexeevD. GaoR. M. HaralickK. ShanmugamI. DinsteinY. LuoS. al-DossaryM. MarhoonM. AlfarajY. LuoW. G. HiggsW. S. KowalikK. J. MarfurtK. J. MarfurtR. L. KirlinR. M. MitchumC. E. PaytonG. PartykaT. B. ReedD. HussongJ. ReillyB. WestS. MayJ. E. EastwoodC. RossenM. B. WidessA. A. Adeogba, T. R. McHargue, S. A. Graham, C. H. Blumentritt, E. C. Sullivan, K. J. Marfurt, S. Chopra, V. Alexeev, D. Gao, R. M. Haralick, K. Shanmugam, I. Dinstein, Y. Luo, S. al-Dossary, M. Marhoon, M. Alfaraj, Y. Luo, W. G. Higgs, W. S. Kowalik, K. J. Marfurt, K. J. Marfurt, R. L. Kirlin, R. M. Mitchum, C. E. Payton, G. Partyka, T. B. Reed, D. Hussong, J. Reilly, B. West, S. May, J. E. Eastwood, C. Rossen, and M. B. Widesshttps://doi.org/10.1190/1.9781560801900.ch5 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Introduction In Chapter 2, we examined how dip and azimuth can quantify lateral changes in reflector time (or depth). In Chapter 3, we examined how coherence can quantify lateral changes in reflector waveform. In the first section of this chapter, we will examine the third family of geometric attributes that measure trace-to-trace variation — those attributes that are sensitive to lateral changes in amplitude. A primary goal of this book is to clearly define the physical and mathematical basis of the seismic attributes commonly used in interpretation, so that we can relate them better to geologic lithology and fluid properties via geostatistics. We also will show that many of the mathematically independent attributes, some of which we have discussed already and some that we introduce in this chapter, may be linked through the underlying geology. Whereas any one of these three families of attributes may be sensitive to thick channels and faults, their responses to subtler features vary. In particular, many thin, lithologically heterogeneous gas-charged reservoirs can be characterized by strong reflection events that have a constant waveform. Such heterogeneities are not seen by crosscorrelation or eigenstructure estimates of coherence, but they are expressed through subtle changes in waveform amplitude. Likewise, subtle changes in thin-bed thickness also cause variations in waveform amplitude. Geometric attributes that estimate dip, azimuth, curvature, and energy-weighted coherent-amplitude gradients (discussed in this chapter) provide valuable information about coherent reflectors. Coherence attributes provide excellent images of discrete discontinuities, but do not discriminate between high-energy low-coherence zones [such as a mass-transport complex (MTC) or a slump] and low-energy low-coherence zones (such as often occur with shale-on-shale reflections). We address these other kinds of textures in the second section of this chapter. This latter type of analysis leads us into the area of statistical pattern recognition, which, along with the morphological pattern recognition discussed in Chapters 2 through 4 (angular unconformities detected using dip and azimuth, meandering-channel edges using coherence, etc.), provides the building blocks for computer-assisted interpretation. Permalink: https://doi.org/10.1190/1.9781560801900.ch5 References Adeogba, A. A. , T. R. McHargue, S. A. Graham, 2005, Transient fan architecture and depositional controls from near-surface 3-D seismic data, Niger Delta continental slope: AAPG Bulletin, 89, 627–643. CrossrefGoogle Scholar Blumentritt, C. H. , E. C. Sullivan, and K. J. Marfurt, 2003, Channel detection using seismic attributes on the Central Basin Platform, west Texas: 73rd Annual International Meeting, SEG, Expanded Abstracts, 466–469. AbstractGoogle Scholar Chopra, S., and V. Alexeev, 2005, Applications of texture attributes to 3D seismic data: CSEG Recorder, 30, 28–32. Google Scholar Gao, D., 2003, Volume texture extraction for 3-D seismic visualization and interpretation: Geophysics, 68, 1294–1302. AbstractGoogle Scholar Haralick, R. M. , K. Shanmugam, and I. Dinstein, 1973, Textural features for image classification: IEEE Transactions: Systems, Man, and Cybernetics, SMC-3, 610–621. CrossrefGoogle Scholar Luo, Y., S. al-Dossary, M. Marhoon, and M. Alfaraj, 2003, Generalized Hilbert transform and its application in geophysics: The Leading Edge, 22, 198–202. AbstractGoogle Scholar Luo, Y., W. G. Higgs, and W. S. Kowalik, 1996, Edge detection and stratigraphic analysis using 3-D seismic data: 66th Annual International Meeting, SEG, Expanded Abstracts, 324–327. AbstractGoogle Scholar Marfurt, K. J. , 2006, Robust estimates of 3D reflector dip and azimuth: Geophysics, 71, 29–40. AbstractGoogle Scholar Marfurt, K. J. , and R. L. Kirlin, 2000, 3-D broadband estimates of reflector dip and amplitude: Geophysics, 65, 304–320. AbstractGoogle Scholar Mitchum, R. M. , 1977, Seismic stratigraphy and global changes of sea level, Part I: Glossary of terms used in seismic stratigraphy, in C. E. Payton, ed., Seismic stratigraphy: Applications to hydrocarbon exploration: AAPG Memoir 26, 205–212. Google Scholar Partyka, G., 2001, Seismic thickness estimation: three approaches, pros and cons: 71st Annual International Meeting, SEG, Expanded Abstracts, 503–506. AbstractGoogle Scholar Reed, T. B. , and D. Hussong, 1989, Digital image processing techniques for enhancement and classification of SeaMARCII side-scan sonar imagery: Journal of Geophysical Research, 94, 7469–7490. CrossrefGoogle Scholar Reilly, J., 2002, 3-D prestack data mining to meet emerging challenges: 72nd Annual International Meeting, SEG, Expanded Abstracts, 476–479. AbstractGoogle Scholar West, B., S. May, J. E. Eastwood, and C. Rossen, 2002, Interactive seismic facies classification using textural and neural networks: The Leading Edge, 21, 1042–1049. AbstractGoogle Scholar Widess, M. B. , 1973, How thin is a thin bed?: Geophysics, 38, 1176–1254. AbstractGoogle ScholarFiguresReferencesRelatedDetails Seismic Attributes for Prospect Identification and Reservoir CharacterizationISBN (print):978-1-56080-141-2ISBN (online):978-1-56080-190-0Copyright: 2007 Pages: 481 publication data© 2007 All rights reserved. No part of this publication may be reproduced or distributed in any form or by any means without written permission of the publisherPublisher:Society of Exploration Geophysicists HistoryPublished in print: 01 Jan 2007 CITATION INFORMATION (2007), "5. Lateral Changes in Amplitude and Pattern Recognition," Geophysical Developments Series : 99-122. https://doi.org/10.1190/1.9781560801900.ch5 Plain-Language Summary PDF DownloadLoading ...