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SeisART software: seismic facies analysis by contributing interpreter and computer

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Abstract

In this paper, an interpreter computer interactive software, named SeisART, is introduced which is employed for seismic facies analysis. Seismic facies analysis is considered as a technique for mapping geological changes using seismic data. In recent years, the number of seismic attributes and the size of seismic data have been increased. Therefore, the interpretation of seismic facies has become a more elusive issue. In a seismic facies analysis procedure, there are three main steps: (i) selecting appropriate attributes as the feature extraction task, (ii) defining the proper number of clusters, and (iii) utilizing an appropriate pattern recognition method. Interpreter plays a remarkable role in performing these steps, based on his/her knowledge, and also available tools and geological information. SeisART as a package-deal software includes numerous methods of feature extraction, validation, and pattern recognition. In a user-friendly environment, the interpreter is able to employ these utilities along with well-designed visualization tools for choosing the best methods to obtain and compare numerous results. Besides, owing to the uncertainty of seismic data, SeisART employs the Fuzzy system to gain more confidence with the seismic facies interpretation.

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References

  • Balasko B, Abonyi J, Feil B (2005) Fuzzy clustering and data analysis toolbox. Dep. Process Eng. Univ, Veszprem Veszprem

    Google Scholar 

  • Barnes AE (2007) Redundant and useless seismic attributes. Geophysics 72:33–P38

    Google Scholar 

  • Barnes AE, Laughlin KJ et al (2002) Investigation of methods for unsupervised classification of seismic data. In: 72 Th annual international meeting. SEG, Expanded Abstracts, pp 2221–2224

    Google Scholar 

  • Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10:191–203

    Article  Google Scholar 

  • Brown AR (2011) Interpretation of three-dimensional seismic data. Society of Exploration Geophysicists and American Association of Petroleum Geologists, Dallas

  • Caliński T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat-Theory Methods 3:1–27

    Article  Google Scholar 

  • Chopra S, Marfurt KJ (2005) Seismic attributes—a historical perspective. Geophysics 70:3SO–28SO

    Article  Google Scholar 

  • Coléou T, Poupon M, Azbel K (2003) Unsupervised seismic facies classification: a review and comparison of techniques and implementation. Lead Edge 22:942–953

    Article  Google Scholar 

  • Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell:224–227

  • de Matos MC, Osorio PL, Johann PR (2006) Unsupervised seismic facies analysis using wavelet transform and self-organizing maps. Geophysics 72:P9–P21

    Article  Google Scholar 

  • Doost MS, Erfani F, Hashemi H, Sokooti M, Sadiq-Arabani M (2013) Gas hydrate detection in the Iranian sector of the Oman Sea: application of AVO and seismic pattern recognition methods. J Seism Explor 22

  • Dorrington KP, Link CA (2004) Genetic-algorithm/neural-network approach to seismic attribute selection for well-log prediction. Geophysics 69:212–221

    Article  Google Scholar 

  • Du H, Cao J, Xue Y, Wang X (2015) Seismic facies analysis based on self-organizing map and empirical mode decomposition. J Appl Geophys 112:52–61

    Article  Google Scholar 

  • Figueiredo AM Silva PM, Gattass M, Silva FB, Milidiú RL (2014) A seismic faces analysis approach to map 3D seismic horizons. In: SEG Technical Program Expanded Abstracts 2014. Society of Exploration Geophysicists, Dallas, pp 1501–1505

  • Gath I, Geva AB (1989) Unsupervised optimal fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 11:773–780

    Article  Google Scholar 

  • Guillen P, Larrazabal G, González G, Boumber D, Vilalta R (2015) Supervised learning to detect salt body. In: SEG Technical Program Expanded Abstracts 2015. Society of Exploration Geophysicists, Dallas, pp 1826–1829

  • Gustafson DE, Kessel WC (1979) Scientific Systems, Inc. 186 Alewife Brook Parlcway, Cambridge, Massachusetts, p 02138

    Google Scholar 

  • Hadiloo S, Hashemi H (2016) Combining supervised and unsupervised method with expert knowledge for seismic facies analysis in SeisAnfis software. In: 78th EAGE Conference and Exhibition 2016. https://doi.org/10.3997/2214-4609.201601629

  • Hashemi H (2010) Logical considerations in applying pattern recognition techniques on seismic data: precise ruling, realistic solutions. Cseg Rec 35:47–50

    Google Scholar 

  • Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685

    Article  Google Scholar 

  • Jayaram V, Avseth PA, Azbel K, Coléou T, Devegowda D, de Groot P, Gao D, Marfurt K, Matos M, Mukerji T, Poupon M (2015) Introduction to special section: pattern recognition and machine learning. Interpretation 3:SAEi–SAEii

  • Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis. John Wiley & Sons, New Jersey

  • Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480

  • Li Y, Chen M, Gao W, Zhong G (2014) Seismic attributes blending and borehole image facies analysis for reservoir prediction: case study for a sand-conglomerate reservoir in Pinghu oil field, East China Sea Basin. In: SEG Technical Program Expanded Abstracts 2014. Society of Exploration Geophysicists, Dallas, pp 2631–2635

  • Li X, Chen Q, Wu C, Liu H, Fang Y (2016) Application of multi-seismic attributes analysis in the study of distributary channels. Mar Pet Geol 75:192–202

    Article  Google Scholar 

  • Marfurt KJ (2014) Seismic attributes and the road ahead. In: SEG Technical Program Expanded Abstracts 2014. Society of Exploration Geophysicists, Dallas, pp 4421–4426

  • Marroquín ID (2014) A knowledge-integration framework for interpreting seismic facies. Interpretation 2:SA1–SA9

    Article  Google Scholar 

  • McLachlan G, Peel D (2004) Finite mixture models. John Wiley & Sons, New York

  • Nikravesh M, Aminzadeh F (2001) Past, present and future intelligent reservoir characterization trends. J Pet Sci Eng 31:67–79

    Article  Google Scholar 

  • Orozco-del-Castillo MG, Ortiz-Alemán C, Urrutia-Fucugauchi J, Rodríguez-Castellanos A (2011) Fuzzy logic and image processing techniques for the interpretation of seismic data. J Geophys Eng 8:185

    Article  Google Scholar 

  • Ross TJ (2009) Fuzzy logic with engineering applications. John Wiley & Sons, West Sussex

  • Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  • Roy A, Jayaram V, Marfurt KJ (2013) Active learning algorithms in seismic facies classification. In: SEG Technical Program Expanded Abstracts 2013. Society of Exploration Geophysicists, Dallas, pp 1467–1471

  • Saggaf MM, Toksöz MN, Marhoon MI (2003) Seismic facies classification and identification by competitive neural networks. Geophysics 68:1984–1999

    Article  Google Scholar 

  • Thenin D, Larson R (2013) Quantitative seismic interpretation—an earth modeling perspective. CSEG Rec 38:30–35

    Google Scholar 

  • Wang W, Zhang Y (2007) On fuzzy cluster validity indices. Fuzzy Sets Syst 158:2095–2117

    Article  Google Scholar 

  • West BP, May SR, Eastwood JE, Rossen C (2002) Interactive seismic facies classification using textural attributes and neural networks. Lead Edge 21:1042–1049

    Article  Google Scholar 

  • White RE (1991) Properties of instantaneous seismic attributes. Lead Edge 10:26–32

    Article  Google Scholar 

  • Yenugu M, Marfurt KJ, Matson S (2010) Seismic texture analysis for reservoir prediction and characterization. Lead Edge 29:1116–1121

    Article  Google Scholar 

  • Zhao T, Ramachandran K (2013) Performance evaluation of complex neural networks in reservoir characterization: applied to Boonsville 3-D seismic data. In: SEG Technical Program Expanded Abstracts 2013. Society of Exploration Geophysicists, Dallas, pp 2621–2625

  • Zhao T, Jayaram V, Roy A, Marfurt KJ (2015) A comparison of classification techniques for seismic facies recognition. Interpretation 3:SAE29–SAE58

    Article  Google Scholar 

Download references

Acknowledgments

We thank the Academic Center for Education, Culture and Research for academic support of the research that is part of the first author’s PhD thesis. We thank Dr. M. Radad for his valuable comments and suggestions in improving the paper and H. Khoshdel in analyzing seismic data.

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Authors and Affiliations

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

    S. Hadiloo & S. Mirzaei

  2. Institute of Geophysics, University of Tehran, Tehran, Iran

    H. Hashemi

  3. Research Institute of Petroleum Industry, Tehran, Iran

    B. Beiranvand

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  1. S. Hadiloo
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  2. H. Hashemi
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  3. S. Mirzaei
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  4. B. Beiranvand
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Correspondence to S. Hadiloo or S. Mirzaei.

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Hadiloo, S., Hashemi, H., Mirzaei, S. et al. SeisART software: seismic facies analysis by contributing interpreter and computer. Arab J Geosci 10, 519 (2017). https://doi.org/10.1007/s12517-017-3274-8

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  • DOI: https://doi.org/10.1007/s12517-017-3274-8

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