Seismic Facies Analysis

Seismic facies are groups of seismic properties and conformity layers that will have a certain relationship with geologic and lithologic properties.

Used ML Approaches:

• Support Vector Machine

• Decision Tree

• Gaussian Mixture Model

• K‐Means

• Principal Component Analysis

• Self Organized Mapping

• Artificial Neural Networks

Used Neural Networks:

• FC

• SOM

• CNN

• RNN

• gradient boosting

• Autoencoder

• Malenov

• LSTM

• U-Net

• Attention

• GAN

• SegNet

• DeepLab

Used Learning Procedures:

• Supervised Learning

• Unsupervised Learning

• Semi-Supervised Learning

• Ensemble Learning

References:

1. Liu, X. Y., Zhou, L., Chen, X. H., & Li, J. Y. (2020). Lithofacies identification using support vector machine based on local deep multi-kernel learning. Petroleum Science, 17(4), 954-966.

2. Liu, X., Chen, X., Li, J., Zhou, X., & Chen, Y. (2020). Facies identification based on multikernel relevance vector machine. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7269-7282.

3. Saporetti, C. M., da Fonseca, L. G., & Pereira, E. (2019). A lithology identification approach based on machine learning with evolutionary parameter tuning. IEEE Geoscience and Remote Sensing Letters, 16(12), 1819-1823.

4. Feng, R., Luthi, S. M., Gisolf, D., & Angerer, E. (2018). Reservoir lithology determination by hidden Markov random fields based on a Gaussian mixture model. IEEE Transactions on Geoscience and Remote Sensing, 56(11), 6663-6673.

5. Wrona, T., Pan, I., Gawthorpe, R. L., & Fossen, H. (2018). Seismic facies analysis using machine learning. Geophysics, 83(5), O83-O95.

6. Galvis, I. S., Villa, Y., Duarte, C., Sierra, D., & Agudelo, W. (2017). Seismic attribute selection and clustering to detect and classify surface waves in multicomponent seismic data by using k-means algorithm. The Leading Edge, 36(3), 239-248.

7. Hall, B. (2016). Facies classification using machine learning. The Leading Edge, 35(10), 906-909.

8. 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.

9. West, B. P., May, S. R., Eastwood, J. E., & Rossen, C. (2002). Interactive seismic facies classification using textural attributes and neural networks. The Leading Edge, 21(10), 1042-1049.

10. Caers, J., & Ma, X. (2002). Modeling conditional distributions of facies from seismic using neural nets. Mathematical Geology, 34(2), 143-167.

11. Saggaf, M. M., Toksöz, M. N., & Marhoon, M. I. (2003). Seismic facies classification and identification by competitive neural networks. Geophysics, 68(6), 1984-1999.

12. de Matos, M. C., Osorio, P. L., & Johann, P. R. (2007). Unsupervised seismic facies analysis using wavelet transform and self-organizing maps. Geophysics, 72(1), P9-P21.

13. Saraswat, P., & Sen, M. K. (2012). Artificial immune-based self-organizing maps for seismic-facies analysis. Geophysics, 77(4), O45-O53.

14. Roden, R., Smith, T., & Sacrey, D. (2015). Geologic pattern recognition from seismic attributes: Principal component analysis and self-organizing maps. Interpretation, 3(4), SAE59-SAE83.

15. Zhao, T., Jayaram, V., Roy, A., & Marfurt, K. J. (2015). A comparison of classification techniques for seismic facies recognition. Interpretation, 3(4), SAE29-SAE58.

16. Zhao, T., Zhang, J., Li, F., & Marfurt, K. J. (2016). Characterizing a turbidite system in Canterbury Basin, New Zealand, using seismic attributes and distance-preserving self-organizing maps. Interpretation, 4(1), SB79-SB89.

17. Ross, C. P., & Cole, D. M. (2017). A comparison of popular neural network facies-classification schemes. The Leading Edge, 36(4), 340-349.

18. Zhao, T., Li, F., & Marfurt, K. J. (2017). Constraining self-organizing map facies analysis with stratigraphy: An approach to increase the credibility in automatic seismic facies classification. Interpretation, 5(2), T163-T171.

19. Zhao, T., Li, F., & Marfurt, K. J. (2018). Seismic attribute selection for unsupervised seismic facies analysis using user-guided data-adaptive weights. Geophysics, 83(2), O31-O44.

20. Qian, F., Yin, M., Liu, X. Y., Wang, Y. J., Lu, C., & Hu, G. M. (2018). Unsupervised seismic facies analysis via deep convolutional autoencoders. Geophysics, 83(3), A39-A43.

21. Duan, Y., Zheng, X., Hu, L., & Sun, L. (2019). Seismic facies analysis based on deep convolutional embedded clustering. Geophysics, 84(6), IM87-IM97.

22. Feng, R., Balling, N., Grana, D., Dramsch, J. S., & Hansen, T. M. (2021). Bayesian Convolutional Neural Networks for Seismic Facies Classification. IEEE Transactions on Geoscience and Remote Sensing.

23. Grana, D., Azevedo, L., & Liu, M. (2020). A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic data. Geophysics, 85(4), WA41-WA52.

24. Li, F., Zhou, H., Wang, Z., & Wu, X. (2020). ADDCNN: An attention-based deep dilated convolutional neural network for seismic facies analysis with interpretable spatial–spectral maps. IEEE Transactions on Geoscience and Remote Sensing, 59(2), 1733-1744.

25. Lin, J., Li, H., Liu, N., Gao, J., & Li, Z. (2020). Automatic lithology identification by applying LSTM to logging data: A case study inX tight rock reservoirs. IEEE Geoscience and Remote Sensing Letters.

26. Liu, M., Jervis, M., Li, W., & Nivlet, P. (2020). Seismic facies classification using supervised convolutional neural networks and semisupervised generative adversarial networks. Geophysics, 85(4), O47-O58.

27. Liu, Z., Cao, J., Lu, Y., Chen, S., & Liu, J. (2019). A seismic facies classification method based on the convolutional neural network and the probabilistic framework for seismic attributes and spatial classification. Interpretation, 7(3), SE225-SE236.

28. Zhang, Y., Liu, Y., Zhang, H., & Xue, H. (2019). Seismic facies analysis based on deep learning. IEEE Geoscience and Remote Sensing Letters, 17(7), 1119-1123.

29. Zhang, H., Chen, T., Liu, Y., Zhang, Y., & Liu, J. (2021). Automatic seismic facies interpretation using supervised deep learning. Geophysics, 86(1), IM15-IM33.

30. Di, H., Gao, D., & AlRegib, G. (2019). Developing a seismic texture analysis neural network for machine-aided seismic pattern recognition and classification. Geophysical Journal International, 218(2), 1262-1275.