An Analysis of Deep Neural Network Model in Recognition of Mud Cuttings Image for Practical Applications

Document Type : Research Paper

Authors

1 Department of Petroleum Engineering, Panjin Vocational and Technical College, Panjin, Liaoning, China

2 CCDC Changqing General Drilling Company, Xi’an, Shaanxi, China

3 Department of Oil and Gas Engineering, Liaoning Petrochemical University, Fushun, Liaoning, China\ Faculty of Engineering and Applied Science, University of Regina, Regina, Canada

4 Department of Oil and Gas Engineering, Liaoning Petrochemical University, Fushun, Liaoning, China

5 Faculty of Engineering and Applied Science, University of Regina, Regina, Canada

Abstract

Traditional mud logging cuttings identification relies on professionals to carry out visual identification and analysis based on experience. The workload is large and subject to the influence of subjectivity, which is likely to cause errors in information extraction and result analysis. Based on applying deep learning theory in image processing technology, ResNet, DenseNet, and SqeezeNet deep neural network models were built according to the classification of cuttings images. The deep neural network models were used to identify the pictures of cuttings subdivision classification. The evaluation indexes, such as stability, robustness, and recognition effect of different models, were compared and analyzed, and the three models were selected according to the best. The results showed that under the Top-2 standard, the deep neural network model was more accurate in recognizing composite cuttings images. In contrast, the SqeezeNet 1_0 model had the best performance in identifying cuttings after synthesizing different evaluation indicators. The final recognition rate of the optimized SqeezeNet 1_0 model reaches 99.48%. In addition, the obtained SqeezeNet 1_0 network model can effectively identify sandstone, mudstone, and conglomerate cuttings on-site and can be extended to the daily identification of composite cuttings.

Keywords

References

  1. Turlapaty, A., Anantharaj, V., & Younan, N. (2010). A pattern recognition-based approach to consistency analysis of geophysical datasets. Computers & Geosciences, 36(4), 464-476. ##
  2. Messina, A., & Langer, H. (2011). Pattern recognition of volcanic tremor data on Mt. Etna (Italy) with KKAnalysis-a software program for unsupervised classification. Computers & Geosciences, 37(7), 953-961. ##
  3. Friedman, M., & Kandel, A. (1999). Introduction to pattern recognition: statistical, structural, neural and fuzzy logic approaches(vol. 32). World Scientific Publishing Company. ##
  4. Milewski, R. J., Govindaraju, V., & Bhardwa, A. (2009). Automatic recognition of handwritten medical forms for search engines. International Journal on Document Analysis and Recognition, 11(4), 203-218. ##
  5. Jimenez, A. R., Jain, A. K., Ceres, J. L., & Pons, J. L. (1999). Automatic fruit recognition: a survey and new results using range/attenuation images. Pattern Recognition, 32(10), 1719-1736. ##
  6. Bressem, K. K., Adams, L. C., Erxleben, C., Hamm, B., Niehues, S. M., & Vahldiek, J. L. (2020). Comparing different deep learning architectures for classification of chest radiographs. Scientific reports, 10(1), 13590. ##
  7. Hasan, N., Bao, Y., Shawon, A., & Huang, Y. (2021). DenseNet convolutional neural networks application for predicting COVID-19 using CT image. SN computer science, 2(5), 389. ##
  8. Yang, Y., Zhang, L., Du, M., Bo, J., Liu, H., Ren, L., & Deen, M. J. (2021). A comparative analysis of eleven neural networks architectures for small datasets of lung images of COVID-19 patients toward improved clinical decisions. Computers in Biology and Medicine, 139, 104887. ##
  9. Marschallinger, R., & Hofmann, P. (2010). The application of object-based image analysis to petrographic micrographs. Microscopy: Science, technology, applications and education, 1526-1532. ##
  10. Hofmann, P., Marschallinger, R., Unterwurzacher, M., & Zobl, F. (2013). Marble provenance designation with object based image analysis: State-of-the-art rock fabric characterization from petrographic micrographs. Austrian Journal of Earth Sciences, 106(2), 73-82. ##
  11. Młynarczuk, M. (2010). Description and classification of rock surfaces by means of laser profilometry. International Journal of Rock Mechanics and Mining Sciences, 47(1), 138-149. ##
  12. Ghiasi-Freez, J., Soleimanpour, I., Kadkhodaie-Ilkhchi, A., Ziaii, M., Sedighi, M., & Hatampour, A. (2012). Semi-automated porosity identification from thin section images using image analysis and intelligent discriminant classifiers. Computers & Geosciences, 45, 36-45. ##
  13. Thompson, S., Fueten, F., & Bockus, D. (2001). Mineral identification using artificial neural networks and the rotating polarizer stage. Computers & Geosciences, 27(9), 1081-1089. ##
  14. Marmo, R., Amodio, S., Tagliaferri, R., Ferreri, V., & Longo, G. (2005). Textural identification of carbonate rocks by image processing and neural network: Methodology proposal and examples, Computers & Geosciences, 31(5), 649-659. ##
  15. Singh, V., & Rao, S. M. (2005). Application of image processing and radial basis neural network techniques for ore sorting and ore classification. Minerals Engineering, 18(15), 1412-1420. ##
  16. Singh, N., Singh, T. N., Tiwary, A., & Sarkar, K. M. (2010). Textural identification of basaltic rock mass using image processing and neural network. Computational Geosciences, 14(2), 301-310. ##
  17. Ishikawa, S. T., & Gulick, V. C. (2013). An automated mineral classifier using Raman spectra. Computers & Geosciences, 54, 259-268. ##
  18. Dunlop, H. (2006). Automatic rock detection and classification in natural scenes. Masters Thesis, Carnegie Mellon University. ##
  19. Baykan, N. A., & Yılmaz, N. (2010). Mineral identification using color spaces and artificial neural networks. Computers & Geosciences, 36(1), 91-97. ##
  20. Młynarczuk, M., Górszczyk, A., & Ślipek, B. (2013). The application of pattern recognition in the automatic classification of microscopic rock images. Computers & Geosciences, 60, 126-133. ##
  21. Izadi, H., Sadri, J., & Bayati, M. (2017). An intelligent system for mineral identification in thin sections based on a cascade approach. Computers & Geosciences, 99, 37-49.
  22. Mikolov, T., Karafiát, M., Burget, L., Cernocký, J., & Khudanpur, S. (2010, September). Recurrent neural network-based language model. In Interspeech, 2(3), 1045-1048. ##
  23. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. ##
  24. Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., & Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29(6), 82-97. ##
  25. Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size, arXiv preprint arXiv, 1602.07360, doi.org/10.48550/arXiv.1602.07360. ##
  26. Han, S., Mao, H., & Dally, W. J. (2015). Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, arXiv preprint arXiv:1510.00149, doi.org/10.48550/arXiv.1510.00149. ##
  27. Li, Y., Zhang, Y., Xu, Y., Miao, Z., & Li, H. (2017, April). Does resnet learn good general-purpose features? In Proceedings of the 2017 International Conference on Artificial Intelligence, Automation and Control Technologies, 1-5. ##
  28. Zhang, S., Yang, H., & Yin, Z. (2015). Multiple deep convolutional neural networks averaging for face alignment, Journal of Electronic Imaging, 24(3), 033013. ##
  29. Arel, I., Rose, D. C., & Karnowski, T. P. (2010). Research frontier: deep machine learning--a new frontier in artificial intelligence research, IEEE Computational Intelligence Magazine, 5(4), 13-18. ##
  30. Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 7132-7141. ##

Files

Share

How to cite

Statistics