Comparison Between Unsupervised and Supervise Fuzzy Clustering Method in Interactive Mode to Obtain the Best Result for Extract Subtle Patterns from Seismic Facies Maps

Document Type: Research Paper

Authors

1 Research Institute of Applied Sciences, (ACECR), Shahid Beheshti University,Tehran,Iran

2 Research Institute of Applied Sciences (ACECR), Shahid Beheshti University, Tehran, Iran

3 Institute of Geophysics, University of Tehran,Iran

4 Research Institute of Petroleum Industry, Tehran, Iran

Abstract

Pattern recognition on seismic data is a useful technique for generating seismic facies maps that capture changes in the geological depositional setting. Seismic facies analysis can be performed using the supervised and unsupervised pattern recognition methods. Each of these methods has its own advantages and disadvantages. In this paper, we compared and evaluated the capability of two unsupervised methods Fuzzy c-means (FCM) and Gustafson Kessel (GK) and one supervised method Adaptive Neuro-Fuzzy Inference Systems (ANFIS) at revealing the presence of a channel system. The process is performed in an interactive scheme in the SeisART software to obtain the best output. The seismic facies analysis was conducted on a 3D seismic data set acquired at North Sea block F3. Based on the results, the GK method outperformed the other two methods in delineating the channel pattern.

Keywords


Article Title [Persian]

-

Author [Persian]

  • سعید هادیلو 1
1 پژوهشکده علوم پایه کاربردی جهاد دانشگاهی، دانشگاه شهید بهشتی
Abraham, A., 2005. Adaptation of fuzzy inference system using neural learning. Fuzzy systems engineering, 914-914.##

Aminzadeh, F., Chatterjee, S., 1984. Applications of clustering in exploration seismology. Geoexploration, 23(1):147-159.##

Balasko, B., Abonyi, J., Feil, B., 2005. Fuzzy clustering and data analysis toolbox. Department of Process Engineering, University of Veszprem, Veszprem.##

Barnes, A.E., Laughlin, K.J., 2002. Investigation of methods for unsupervised classification of seismic data. In SEG Technical Program Expanded Abstracts 2002: 2221-2224.##

Barnes, A. E., 2007. Redundant and useless seismic attributes. Geophysics, 72(3): 33-38.##

Bezdek, J. C., 1980. A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms. IEEE transactions on pattern analysis and machine intelligence, 2(1): 1-8.##

Bezdek, J.C., 2013. Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media.##

Chen, M. S., 1999. A comparative study of learning methods in tuning parameters of fuzzy membership functions. In Systems, Man, and Cybernetics, 1999. IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on, (3): 40-44.##

Chopra, S., Marfurt, K.J., 2005. Seismic attributes—A historical perspective. Geophysics, 70: 3SO–28SO.##

Coléou, T., Poupon, M., Azbel, K., 2003. Unsupervised seismic facies classification: A review and comparison of techniques and implementation. The Leading Edge, 22(10): 942-953.##

de Matos, M.C., Osorio, P.L., Johann, P.R., 2006. Unsupervised seismic facies analysis using wavelet transform and self-organizing maps. Geophysics, 72: 9–21.##

dGB Earth Sciences B.V., 2013. Introduction to OpendTect V. 4.4 F3-Dutch Offshore.##

Dorrington, K.P., Link, C.A., 2004. Genetic-algorithm/neural-network approach to seismic attribute selection for well-log prediction. Geophysics, 69: 212–221.##

Dunn, J. C., 1973. A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters, Journal of Cybernetics, 3: 32-57##

Figueiredo, A. M., Silva, F. B., Silva, P. M., Milidiú, R. L., Gattass, M., 2014. A Seismic Facies Analysis Approach to Map 3D Seismic Horizons. In 2014 SEG Annual Meeting.##

Ghosh, A., Mishra, N. S., Ghosh, S., 2011. Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Information Sciences, 181(4): 699-715.##

Guillen, P., Larrazabal, G., González, G., Boumber, D., Vilalta, R., others, 2015. Supervised learning to detect salt body, in: 2015 SEG Annual Meeting.##

Gustafson, D.E., Kessel, W.C., 1979. January. Fuzzy clustering with a fuzzy covariance matrix. In Decision and Control including the 17th Symposium on Adaptive Processes, 1978 IEEE Conference on, 761-766.##

Hadiloo, S., Shahdani, H., 2016. Combining Supervised and Unsupervised Method with Expert Knowledge for Seismic Facies Analysis in SeisAnfis Software. In 78th EAGE Conference and Exhibition.##

Hadiloo, S., Hashemi, H., Mirzaei, S., Beiranvand, B., 2017. SeisART software: seismic facies analysis by contributing interpreter and computer. Arabian Journal of Geosciences, 10(23): 519.##

Hashemi, H., 2010. Logical considerations in applying pattern recognition techniques on seismic data: Precise ruling, realistic solutions. Cseg Recorder, 35(4): 47-50.##

Jang, J.S., 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3): 665-685.##

Jang, J.S.R., Sun, C.T. Mizutani, E., 1997. Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Transactions on automatic control, 42(10): 1482-1484.##

Kim, E., Park, M., Ji, S. and Park, M., 1997. A new approach to fuzzy modeling. IEEE Transactions on fuzzy systems, 5(3): 328-337.##

Kuralkhanov, D., 2010. Study of Pattern Correlation Between Time Lapse Seismic Data and Saturation Changes (Doctoral dissertation, STANFORD UNIVERSITY).##

Kumar, M., Garg, D.P., 2004. Intelligent learning of fuzzy logic controllers via neural network and genetic algorithm. In Proceedings of, 1-8.##

Lesot, M.J. and Kruse, R., 2008. Gustafson-Kessel-like clustering algorithm based on typicality degrees. In Uncertainty and Intelligent Information Systems, 117-130.##

Marfurt, K.J., others, 2014. Seismic attributes and the road ahead, in: 84th SEG Meeting Expanded Abstracts.##

Marroquín, I.D., 2014. A knowledge-integration framework for interpreting seismic facies. Interpretation, 2: SA1–SA9.##

Nikravesh, M., Aminzadeh, F., 2001. Past, present and future intelligent reservoir characterization trends. Journal of Petroleum Science and Engineering, 31(2): 67-79.##

Orozco-del-Castillo, M.G., Ortiz-Alemán, C., Urrutia-Fucugauchi, J., Rodríguez-Castellanos, A., 2011. Fuzzy logic and image processing techniques for the interpretation of seismic data. Journal of Geophysics and Engineering, 8(2): 185.##

Overeem, I., Weltje, G. J., Bishop‐Kay, C., Kroonenberg, S. B., 2001. The Late Cenozoic Eridanos delta system in the Southern North Sea Basin: a climate signal in sediment supply? Basin Research, 13(3): 293-312.##

Roweis, S.T., Saul, L.K., 2000. Nonlinear dimensionality reduction by locally linear embedding. Science, 290: 2323–2326.##

Roy, A., Jayaram, V., Marfurt, K.J., 2013. Active learning algorithms in seismic facies classification. In 2013 SEG Annual Meeting.##

Saggaf, M.M., Toksöz, M.N., Marhoon, M.I., 2003. Seismic facies classification and identification by competitive neural networks. Geophysics, 68: 1984–1999.##

Song, C., Liu, Z., Wang, Y., Li, X., Hu, G., 2017. Multi-waveform classification for seismic facies analysis. Computers & Geosciences, 101: 1-9.##

Sørensen, J. C., Gregersen, U., Breiner, M., Michelsen, O., 1997. High-frequency sequence stratigraphy of Upper Cenozoic deposits in the central and southeastern North Sea areas. Marine and Petroleum Geology, 14(2): 99-123.##

Takagi, T., Sugeno, M. 1985. Fuzzy identification of systems and its applications to modeling and control. IEEE transactions on systems, man, and cybernetics, (1): 116-132.##

Tamhane, D., Wong P.M., Aminzadeh, F., 2002 Integrating linguistic descriptions and digital signals in petroleum reservoirs Int. J. Fuzzy Syst., 4: 586–91##

Thenin, D., Larson, R., 2013. Quantitative seismic interpretation—an earth modeling perspective. CSEG Recorder, 38: 30-35.##

Tsukamoto, Y., 1979. An approach to fuzzy reasoning method. Advances in fuzzy set theory and applications.##

Gupta, M.M., Ragade, R.K., Yager, R.R. eds., 1979. Advances in fuzzy set theory and applications. North-Holland Publishing Company.##

Wang, W., Zhang, Y., 2007. On fuzzy cluster validity indices. Fuzzy sets and systems, 158(19): 2095-2117.##

West, B.P., May, S.R., Eastwood, J.E., Rossen, C., 2002. Interactive seismic facies classification using textural attributes and neural networks. Lead. Edge, 21: 1042–1049.##

White, R.E., 1991. Properties of instantaneous seismic attributes. Lead. Edge, 10: 26–32.##

Yenugu, M., Marfurt, K.J., Matson, S., 2010. Seismic texture analysis for reservoir prediction and characterization. Lead. Edge, 29: 1116–1121.##

Zhao, T., Jayaram, V., Roy, R., Marfurt, K.J., 2015. A comparison of classification techniques for seismic facies recognition: Interpretation, 3: 29-58.##

Zhao, T., Ramachandran, K., 2013. Performance evaluation of complex neural networks in reservoir characterization: Applied to Boonsville 3-D seismic data. In 2013 SEG Annual Meeting.##