Abstract
Seismic texture attributes are closely related to seismic facies and reservoir characteristics and are thus widely used in seismic data interpretation. However, information is mislaid in the stacking process when traditional texture attributes are extracted from post-stack data, which is detrimental to complex reservoir description. In this study, pre-stack texture attributes are introduced, these attributes can not only capable of precisely depicting the lateral continuity of waveforms between different reflection points but also reflect amplitude versus offset, anisotropy, and heterogeneity in the medium. Due to its strong ability to represent stratigraphics, a pre-stack-data-based seismic facies analysis method is proposed using the self-organizing map algorithm. This method is tested on wide azimuth seismic data from China, and the advantages of pre-stack texture attributes in the description of stratum lateral changes are verified, in addition to the method’s ability to reveal anisotropy and heterogeneity characteristics. The pre-stack texture classification results effectively distinguish different seismic reflection patterns, thereby providing reliable evidence for use in seismic facies analysis.
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References
Chopra, S., and Alexeev, V., 2006, Applications of texture attribute analysis to 3D seismic data: The Leading Edge, 25(8), 934–940.
Chopra, S., and Marfurt, K. J., 2007, Seismic Attributes for Prospect Identification and Reservoir Characterization: 77th Annual International Meeting, SEG, Expanded Abstracts, 114–116.
Chopra, S., and Marfurt, K. J., 2014, Seismic facies analysis using generative topographic mapping: 84th Ann. Internat. Mtg, Soc. Expl. Geopghs., Expanded Abstracts, 1390–1394.
de Matos, M. C., Osorio, P. L., and Johann, P. R., 2006, Unsupervised seismic facies analysis using wavelet transform and self-organizing maps: Geophysics, 72(1), P9–P21.
de Matos, M. C., Marfurt, K. J., and Johann, P. R., 2010, Seismic interpretation of self-organizing maps using 2D color displays: Revista Brasileira de Geofísica, 28(4), 631–642.
de Matos, M. C., Yenugu, M., Angelo, S. M., et al., 2011, Integrated seismic texture segmentation and cluster analysis applied to channel delineation and chert reservoir characterization: Geophysics, 76(5), P11–P21.
Du, H., Cao, J., Xue, Y., et al., 2015, Seismic facies analysis based on self-organizing map and empirical mode decomposition: Journal of Applied Geophysics, 112, 52–61.
Eichkitz, C. G., Schreilechner, M. G., de Groot, P., et al., 2015, Mapping directional variations in seismic character using gray-level co-occurrence matrix-based attributes: Interpretation, 3(1), T13–T23.
Gao, D., 2003, Volume texture extraction for 3D seismic visualization and interpretation: Geophysics, 68(4), 1294–1302.
Gao, D., 2007, Application of three-dimensional seismic texture analysis with special reference to deep-marine facies discrimination and interpretation: Offshore Angola, west Africa., AAPG bulletin, 91(12), 1665–1683.
Gao, D., 2011, Latest developments in seismic texture analysis for subsurface structure, facies, and reservoir characterization: A review: Geophysics, 76(2), W1–W13.
Han, M., Zhao, Y., Li, G., et al., 2011, Application of EM algorithms for seismic facices classification: Computational Geosciences, 15(3), 421–429.
Kourki, M. and Riahi, M. A., 2014, Seismic facies analysis from pre-stack data using self-organizing maps: Journal of Geophysics and Engineering, 11(6), 065005.
Mallick, S., Craft, K. L., Meister, L. J., et al., 1998, Determination of the principal directions of azimuthal anisotropy from P-wave seismic data: Geophysics, 63(2), 692–706.
Marfurt, K. J., 2014, Seismic attributes and the road ahead: 84th Ann. Internat. Mtg, Soc. Expl. Geopghs., Expanded Abstracts, 4421–4426.
Marroquín, I. D., Brault, J., and Hart, B., 2008, A visual data-mining methodology for seismic facies analysis: Part 1-Testing and comparison with other unsupervised clustering methods: Geophysics, 74(1), 1–11.
Roy, A., de Matos, M. C., and Marfurt, K. J., 2010, Automatic Seismic Facies Classification with Kohonen Self Organizing Maps-a Tutorial: Geohorizons Journal of Society of Petroleum Geophysicists, 6–14.
Roy, A., 2013, Latent space classification of seismic facies: PhD Thesis, University of Oklahoma, Oklahoma.
Saraswat, P., and Sen, M. K., 2012, Artificial immunebased self-organizing maps for seismic-facies analysis: Geophysics, 77(4), O45–O53.
Song, C., Li, Z, Liu, Z, et al., 2015, Prestack Reflection Pattern Based Seismic Facies Analysis: 85th Ann. Internat. Mtg, Soc. Expl. Geopghs., Expanded Abstracts, 1633–1637.
West, B. P., May, S. R., Eastwood, J. E., et al., 2002, Interactive seismic facies classification using textural attributes and neural networks: The Leading Edge, 21(10), 1042–1049.
Yan, Z., Zheng, X., Li, J., et al. 2015, Unsupervised seismic facies analysis technology based on SOM and PSO: Chinese Journal of Geophysics, (in Chinese), 58(9), 3412–3423.
Yenugu, M., Marfurt, K. J., and Marson, S., 2010, Seismic texture analysis for reservoir prediction and characterization: The Leading Edge, 29(9), 1116–1121.
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This work was supported by the Scientific Research Staring Foundation of University of Electronic Science and Technology of China (No. ZYGX2015KYQD049).
Song Cheng-Yun, is a PhD candidate who received his M.S. degree in control theory and control engineering from Lanzhou University of Technology in 2010, mainly focusing on the technique of image processing and pattern recognition. He is currently pursuing his Doctoral degree in communication and information system at the University of Electronic Science and Technology of China. His current research interests include seismic attributes and seismic image recognition.
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Song, CY., Liu, ZN., Cai, HP. et al. Pre-stack-texture-based reservoir characteristics and seismic facies analysis. Appl. Geophys. 13, 69–79 (2016). https://doi.org/10.1007/s11770-016-0541-5
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DOI: https://doi.org/10.1007/s11770-016-0541-5