Adaptive phase k-means algorithm for waveform classification
Chengyun Song 1 4 Zhining Liu 1 Yaojun Wang 2 Feng Xu 3 Xingming Li 1 Guangmin Hu 21 School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
2 School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
3 School of Geoscience and Technology, Southwest Petroleum University, Chengdu, Sichuan 610500, China.
4 Corresponding author. Email: scyer123@163.com
Exploration Geophysics 49(2) 213-219 https://doi.org/10.1071/EG16111
Submitted: 20 September 2016 Accepted: 1 December 2016 Published: 4 January 2017
Abstract
Waveform classification is a powerful technique for seismic facies analysis that describes the heterogeneity and compartments within a reservoir. Horizon interpretation is a critical step in waveform classification. However, the horizon often produces inconsistent waveform phase, and thus results in an unsatisfied classification. To alleviate this problem, an adaptive phase waveform classification method called the adaptive phase k-means is introduced in this paper. Our method improves the traditional k-means algorithm using an adaptive phase distance for waveform similarity measure. The proposed distance is a measure with variable phases as it moves from sample to sample along the traces. Model traces are also updated with the best phase interference in the iterative process. Therefore, our method is robust to phase variations caused by the interpretation horizon. We tested the effectiveness of our algorithm by applying it to synthetic and real data. The satisfactory results reveal that the proposed method tolerates certain waveform phase variation and is a good tool for seismic facies analysis.
Key words: horizon interpretation, k-means, phase variation, waveform classification.
References
Andersen, E., and Boyd, J., 2004, Seismic waveform classification: techniques and benefits: CSEG Recorder, 29, 26–29Arshin, B. M., Ghazali, A. R., Amin, Y. K., and Barnes, A. E., 2014, Hybrid waveform classification applied to delineate compartments in a complex reservoir in the Malay Basin: International Petroleum Technology Conference, IPTC-18052-MS, 1–5.
Basman, Y. V. II, 2015, Seismic waveform classification: renewing the interest in Barrolka field, SW Queensland, Cooper Basin: ASEG Extended Abstracts, 1, 1–3.
Chopra, S., and Marfurt, K. J., 2014, Seismic facies analysis using generative topographic mapping: 84th Annual International Meeting, SEG, Expanded Abstracts, 1390–1394.
Coléou, T., Poupon, M., and Azbel, K., 2003, Unsupervised seismic facies classification: a review and comparison of techniques and implementation: The Leading Edge, 22, 942–953
| Unsupervised seismic facies classification: a review and comparison of techniques and implementation:Crossref | GoogleScholarGoogle Scholar |
de Matos, M. C., Osorio, P. L., and Johann, P. R., 2007, Unsupervised seismic facies analysis using wavelet transform and self-organizing maps: Geophysics, 72, P9–P21
| Unsupervised seismic facies analysis using wavelet transform and self-organizing maps:Crossref | GoogleScholarGoogle Scholar |
de Matos, M. C., Yenugu, M, Angelo, S. M., and Marfurt, K. J., 2011, Integrated seismic texture segmentation and cluster analysis applied to channel delineation and chert reservoir characterization: Geophysics, 76, P11–P21
| Integrated seismic texture segmentation and cluster analysis applied to channel delineation and chert reservoir characterization:Crossref | GoogleScholarGoogle Scholar |
Du, H.-k., Cao, J.-x., Xue, Y.-j., and Wang, X.-j., 2015, Seismic facies analysis based on self-organizing map and empirical mode decomposition: Journal of Applied Geophysics, 112, 52–61
| Seismic facies analysis based on self-organizing map and empirical mode decomposition:Crossref | GoogleScholarGoogle Scholar |
Gao, D., 2008, Application of seismic texture model regression to seismic facies characterization and interpretation: The Leading Edge, 27, 394–397
| Application of seismic texture model regression to seismic facies characterization and interpretation:Crossref | GoogleScholarGoogle Scholar |
Gao, D., 2011, Latest developments in seismic texture analysis for subsurface structure, facies, and reservoir characterization: Reviews of Geophysics, 76, W1–W13
| Latest developments in seismic texture analysis for subsurface structure, facies, and reservoir characterization:Crossref | GoogleScholarGoogle Scholar |
Han, M., Zhao, Y., Li, G., and Reynolds, A. C., 2011, Application of EM algorithms for seismic facies classification: Computational Geosciences, 15, 421–429
| Application of EM algorithms for seismic facies classification:Crossref | GoogleScholarGoogle Scholar |
Jain, A. K., 2010, Data clustering: 50 years beyond K-means: Pattern Recognition Letters, 31, 651–666
| Data clustering: 50 years beyond K-means:Crossref | GoogleScholarGoogle Scholar |
Johann, P., de Castro, D. D., and Barroso, A. S., 2001, Reservoir geophysics: seismic pattern recognition applied to ultra-deepwater oilfield in Campos Basin, offshore Brazil: Society of Petroleum Engineers Latin American and Caribbean Petroleum Engineering Conference, SPE-69483-MS, 1–13.
Litvinov, A. Y., 2002, Reservoir characterization from seismic waveform using forward modeling and pattern recognition: The Leading Edge, 21, 1028–1031
| Reservoir characterization from seismic waveform using forward modeling and pattern recognition:Crossref | GoogleScholarGoogle Scholar |
Marroquín, I. D., Brault, J.-J., and Hart, B. S., 2009, A visual data-mining methodology for seismic facies analysis: part 1 – testing and comparison with other unsupervised clustering methods: Geophysics, 74, P1–P11
| A visual data-mining methodology for seismic facies analysis: part 1 – testing and comparison with other unsupervised clustering methods:Crossref | GoogleScholarGoogle Scholar |
Rankey, E., and Mitchell, J., 2003, That’s why it’s called interpretation: impact of horizon uncertainty on seismic attribute analysis: The Leading Edge, 22, 820–828
| That’s why it’s called interpretation: impact of horizon uncertainty on seismic attribute analysis:Crossref | GoogleScholarGoogle Scholar |
Saggaf, M. M., Toksöz, M. N., and Marhoon, M. I., 2003, Seismic facies classification and identification by competitive neural networks: Geophysics, 68, 1984–1999
| Seismic facies classification and identification by competitive neural networks:Crossref | GoogleScholarGoogle Scholar |
Santos, M. S. d., 1997, Caracterização de reservatórios via redes neurais: M.Sc. thesis, Bahia Federal University.
Singh, V. B., Subrahmanyam, D., Negi, S. P. S., Baid, V. K., Kumar, A., and Biswal, S., 2004, Facies classification based on seismic waveform – a case study from Mumbai High North: 5th Conference and Exposition on Petroleum Geophysics, Hyderabad, India, 456–462.
Wallet, B. C., de Matos, M. C., Kwiatkowski, J. T., and Suarez, Y, 2009, Latent space modeling of seismic data: an overview: The Leading Edge, 28, 1454–1459
| Latent space modeling of seismic data: an overview:Crossref | GoogleScholarGoogle Scholar |
Zhao, T., Jayaram, V., Roy, A., and Marfurt, K. J., 2015, A comparison of classification techniques for seismic facies recognition: Interpretation, 3, SAE29–SAE58
| A comparison of classification techniques for seismic facies recognition:Crossref | GoogleScholarGoogle Scholar |