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

Tao Liu, Chunsheng Li, Zongbao Liu, Kejia Zhang, Fang Liu, Dongsheng Li, Yan Zhang, Zhigang Liu, Liyuan Liu and Jiacheng Huang
Additional contact information
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

Energies, 2022, vol. 15, issue 16, 1-16

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.

Keywords: tight oil reservoir; rock thin slices; characteristics identification; deep learning; unconventional oil and gas (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/16/5818/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/16/5818/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:16:p:5818-:d:885221

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().