Kejia Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Kejia Zhang has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Chunsheng Li
School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China
1 shared publication - Dongsheng Li
School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China
1 shared publication - Fang Liu
School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China
1 shared publication - Jiacheng Huang
School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China
1 shared publication - Liyuan Liu
School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China
1 shared publication - Tao Liu
School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China
1 shared publication
Latest Publications
- Open Access
Research on Image Identification Method of Rock Thin Slices in Tight Oil Reservoirs Based on Mask R-CNN
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.
Open AccessResearch on Image Identification Method of Rock Thin Slices in Tight Oil Reservoirs Based on Mask R-CNN
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.