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Research on Image Identification Method of Rock Thin Slices in Tight Oil Reservoirs Based on Mask R-CNN

Author

Listed:
  • Tao Liu

    (School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China)

  • Chunsheng Li

    (School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China)

  • Zongbao Liu

    (School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China)

  • Kejia Zhang

    (School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China)

  • Fang Liu

    (School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China)

  • Dongsheng Li

    (School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China)

  • Yan Zhang

    (School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China)

  • Zhigang Liu

    (School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China)

  • Liyuan Liu

    (School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China)

  • Jiacheng Huang

    (School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China)

Abstract

Terrestrial tight oil has extremely strong diagenesis heterogeneity, so a large number of rock thin slices are needed to reveal the real microscopic pore-throat structure characteristics. In addition, difficult identification, high cost, long time, strong subjectivity and other problems exist in the identification of tight oil rock thin slices, and it is difficult to meet the needs of fine description and quantitative characterization of the reservoir. In this paper, a method for identifying the characteristics of rock thin slices in tight oil reservoirs based on the deep learning technique was proposed. The present work has the following steps: first, the image preprocessing technique was studied. The original image noise was removed by filtering, and the image pixel size was unified by a normalization technique to ensure the quality of samples; second, the self-labeling image data augmentation technique was constructed to solve the problem of sparse samples; third, the Mask R-CNN algorithm was introduced and improved to synchronize the segmentation and recognition of rock thin slice components in tight oil reservoirs; Finally, it was demonstrated through experiments that the SMR method has significant advantages in accuracy, execution speed and migration.

Suggested Citation

  • Tao Liu & Chunsheng Li & Zongbao Liu & Kejia Zhang & Fang Liu & Dongsheng Li & Yan Zhang & Zhigang Liu & Liyuan Liu & Jiacheng Huang, 2022. "Research on Image Identification Method of Rock Thin Slices in Tight Oil Reservoirs Based on Mask R-CNN," Energies, MDPI, vol. 15(16), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5818-:d:885221
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    References listed on IDEAS

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    1. Zhang, Tao & Li, Yiteng & Chen, Yin & Feng, Xiaoyu & Zhu, Xingyu & Chen, Zhangxing & Yao, Jun & Zheng, Yongchun & Cai, Jianchao & Song, Hongqing & Sun, Shuyu, 2021. "Review on space energy," Applied Energy, Elsevier, vol. 292(C).
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