Skip to main content
SpringerLink
Log in

Research on cuttings image segmentation method based on improved MultiRes-Unet++  with attention mechanism

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Cuttings logging is an important technology in petroleum exploration and production. It can be used to identify rock types, oil and gas properties, and reservoir features. However, the cuttings collected during cuttings logging are often small and few. Meanwhile, the surface color of cuttings is dark and the boundary is fuzzy. Traditional image segmentation methods have low accuracy. So it is difficult to identify and classify cuttings. Therefore, it is important to improve the accuracy of cuttings image segmentation. A deep learning-based cuttings image segmentation method is proposed in this paper. Firstly, the MultiRes module concept based on the UNet++ segmentation model is introduced in this paper, which proposes an improved end-to-end UNet++ image semantic segmentation model (called MultiRes-UNet++). Secondly, batch normalization into the input part of each layer's feature convolution layer is introduced too. Finally, a convolutional attention mechanism in the improved MultiRes-UNet++ segmentation model is introduced. Experimental results show that the accuracy between the segmentation results and the original image labels is 0.8791, the dice coefficient value is 0.8785, and the intersection over union is 0.7833. Compared with existing neural network segmentation algorithms, the performance is improved by about 5%. Compared with the algorithm before the fusion of the attention mechanism, the training speed is increased by about 75.2%. Our method can provide auxiliary information for cuttings logging. It is also of great significance for subsequent rock identification and classification.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Yang, Z., Zhang, L., Sun, D., Wang, X., Wang, W.:Geological characteristics, development status and technical countermeasures of enhanced oil recovery in loose glutenite heavy oil reservoir. In: Proceedings of The 8th Academic Conference of Geology Resource Management and Sustainable Development, Aussino Academic Publishing House, pp. 362–373 (2020)

  2. Mao, T., Gu, X., Zhu, S.: Particle size analysis based on improved watershed algorithm. Electronic Devices 31, 1369–1372 (2008). https://doi.org/10.3969/j.issn.1005-9490.2008.04.075

    Article  Google Scholar 

  3. Ma, W.Y., Manjunath, B.S.: EdgeFlow: a technique for boundary detection and image segmentation. IEEE Trans. Image Process. 9, 1375–1388 (2000). https://doi.org/10.1109/83.855433

    Article  MathSciNet  Google Scholar 

  4. Liu, M.Y., Tuzel, O., Ramalingam, S., Chellappa, R.: Entropy-rate clustering: cluster analysis via maximizing a submodular function subject to a matroid constraint. IEEE Trans. Pattern Anal. Mach. Intell. 36, 99–112 (2013). https://doi.org/10.1109/TPAMI.2013.107

    Article  Google Scholar 

  5. Samet, R., Amrahov, Ş.E., Ziroğlu, A.H.: Fuzzy rule-based image segmentation technique for rock thin section images. In: 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, pp 402–406 (2012)

  6. Wang, Q., Wang, Z., Fan, Y., Teng, Q., He, X.: Image segmentation of debris particles based on edge flow and region merging. J. Sichuan Univ. (Nat. Sci. Edn.) 51, 111–118 (2014). https://doi.org/10.3969/j.issn.0490-6756.2014.01.019

    Article  Google Scholar 

  7. Sun, Z., Xuan, P., Song, Z., Li, H., Jia, R.: A texture fused superpixel algorithm for coal mine waste rock image segmentation. Int. J. Coal Prep. Util. 42, 1222–1233 (2022). https://doi.org/10.1080/19392699.2019.1699546

    Article  Google Scholar 

  8. Chauhan, S., Rühaak, W., Khan, F., Enzmann, F., Mielke, P., Kersten, M., Sass, I.: Processing of rock core microtomography images: Using seven different machine learning algorithms. Comput. Geosci. 86, 120–128 (2016). https://doi.org/10.1016/j.cageo.2015.10.013

    Article  Google Scholar 

  9. Młynarczuk, M., Górszczyk, A., Ślipek, B.: The application of pattern recognition in the automatic classification of microscopic rock images. Comput. Geosci. 60, 126–133 (2013). https://doi.org/10.1016/j.cageo.2013.07.015

    Article  Google Scholar 

  10. Hossein, I., Javad, S., Mahdokht, B.: An intelligent system for mineral identification in thin sections based on a cascade approach. Comput. Geosci. 99, 37–49 (2017). https://doi.org/10.1016/j.cageo.2016.10.010

    Article  Google Scholar 

  11. Thompson, S., Fueten, F., Bockus, D.: Mineral identification using artificial neural networks and the rotating polarizer stage. Comput. Geosci. 27, 1081–1089 (2001). https://doi.org/10.1016/S0098-3004(00)00153-9

    Article  Google Scholar 

  12. Ye, R., Niu, R., Zhang, L.: Mineral feature extraction and analysis of rock images based on multi-scale segmentation. J. Jilin Univ. (Earth Sci. Edn.) 41, 1253–1261 (2011). https://doi.org/10.13278/j.cnki.jjuese.2011.04.034

  13. Guo, C., Liu, Y.: Rock image recognition in multi-color space. Sci. Technol. Eng. 14, 247–251 (2014). https://doi.org/10.3969/j.issn.1671-1815.2014.18.048

  14. Liu, X., Zhang Y.: Image segmentation method of conveyor ore based on U-NET and RES_U-NET model. J. Northeastern Univ. (Nat. Sci. Edn.) 40, 1623–1629 (2019). CNKI:SUN:DBDX.0.2019-11-019

  15. Tan, Y., Tian, M., Xu, D., Sheng, G., Ma, K., Qiu, Q., Pan, S.: Research on rock image classification and recognition based on Xception network. Geogr. Geogr. Inf. Sci. 38, 17–22 (2022). https://doi.org/10.3969/j.issn.1672-0504.2022.03.003

    Article  Google Scholar 

  16. Ma, Z., Ma, L., Li, K., Yao, W., Wang, P., Wang, X.: Multi-scale lithology identification based on rock image deep learning. Bull. Geol. Sci. Technol. 41, 316–322 (2022). https://doi.org/10.19509/j.cnki.dzkq.2022.0140.

  17. Huo, F., Li, A., Zhao, X., Ren, W., Dong, H., Yang, J.: Novel lithology identification method for drilling cuttings under PDC bit condition. J. Petrol. Sci. Eng. 205, 108898 (2021). https://doi.org/10.1016/j.petrol.2021.108898

    Article  Google Scholar 

  18. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18, Springer International Publishing, pp 234–241 (2015)

  19. Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39, 1856–1867 (2019). https://doi.org/10.1109/TMI.2019.2959609

    Article  Google Scholar 

  20. Ioffe, S., Szegedy, : Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pmlr, pp. 448–456 (2015)

  21. Ibtehaz, N., Rahman, M.S.: MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74–87 (2020). https://doi.org/10.1016/j.neunet.2019.08.025

    Article  Google Scholar 

  22. He, K., Zhang, X., Ren, S., Sun, Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90.

  23. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015). https://doi.org/10.1109/CVPR.2015.7298594.

Download references

Acknowledgements

This work was supported in part by Heilongjiang Provincial Natural Science Foundation of China LH2023F007.

Author information

Authors and Affiliations

  1. National Key Laboratory of Continental Shale Oil, Northeast Petroleum University, Daqing, 163318, Heilongjiang, China

    Fengcai Huo, Kaiming Liu, Hongli Dong, Weijian Ren & Shuai Dong

  2. Artificial Intelligence Energy Institute, Northeast Petroleum University, Daqing, 163318, Heilongjiang, China

    Fengcai Huo, Kaiming Liu, Hongli Dong, Weijian Ren & Shuai Dong

  3. Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Daqing, 163318, Heilongjiang, China

    Fengcai Huo, Kaiming Liu, Hongli Dong & Shuai Dong

Authors
  1. Fengcai Huo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kaiming Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongli Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weijian Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shuai Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The initial draft of this article was written by FH, with KL responsible for conducting related research experiments and summarizing the data. HD summarized, categorized, and analyzed the relevant principles of this article. WR summarized the research materials and data and created corresponding tables and graphs. SD was responsible for revising and formatting the article to produce the final version. All five individuals mentioned above have read the final manuscript and conducted analyses and discussions on it.

Corresponding author

Correspondence to Fengcai Huo.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huo, F., Liu, K., Dong, H. et al. Research on cuttings image segmentation method based on improved MultiRes-Unet++  with attention mechanism. SIViP (2024). https://doi.org/10.1007/s11760-024-03192-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11760-024-03192-3

Keywords

Search

Navigation