Intelligent Identification Method for the Diagenetic Facies of Tight Oil Reservoirs Based on Hybrid Intelligence—A Case Study of Fuyu Reservoir in Sanzhao Sag of Songliao Basin

Tao Liu, Zongbao Liu, Kejia Zhang (), Chunsheng Li, Yan Zhang, Zihao Mu, Fang Liu, Xiaowen Liu, Mengning Mu and Shiqi Zhang
Additional contact information
Tao Liu: 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
Chunsheng 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
Zihao Mu: School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China
Fang Liu: School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China
Xiaowen Liu: School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China
Mengning Mu: School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China
Shiqi Zhang: School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China

Energies, 2024, vol. 17, issue 7, 1-20

Abstract: The diagenetic facies of tight oil reservoirs reflect the diagenetic characteristics and micro-pore structure of reservoirs, determining the formation and distribution of sweet spot zones. By establishing the correlation between diagenetic facies and logging curves, we can effectively identify the vertical variation of diagenetic facies types and predict the spatial variation of reservoir quality. However, it is still challenging work to establish the correlation between logging and diagenetic facies, and there are some problems such as low accuracy, high time consumption and high cost. To this end, we propose a lithofacies identification method for tight oil reservoirs based on hybrid intelligence using the Fuyu oil layer of the Sanzhao depression in Songliao Basin as the target area. Firstly, the geological characteristics of the selected area were analyzed, the definition and classification scheme of diagenetic facies and the dominant diagenetic facies were discussed, and the logging response characteristics of various diagenetic facies were summarized. Secondly, based on the standardization of logging curves, the logging image data set of various diagenetic facies was built, and the imbalanced data set processing was performed. Thirdly, by integrating CNN (Convolutional Neural Networks) and ViT (Visual Transformer), the C-ViTM hybrid intelligent model was constructed to identify the diagenetic facies of tight oil reservoirs. Finally, the effectiveness of the method is demonstrated through experiments with different thicknesses, accuracy and single-well identification. The experimental results show that the C-ViTM method has the best identification effect at the sample thickness of 0.5 m, with Precision of above 86%, Recall of above 90% and F 1 score of above 89%. The calculation result of the Jaccard index in the identification of a single well was 0.79, and the diagenetic facies of tight reservoirs can be identified efficiently and accurately. At the same time, it also provides a new idea for the identification of the diagenetic facies of old oilfields with only logging image data sets.

Keywords: tight oil reservoirs; diagenetic phases; log recognition; hybrid intelligence; reservoir prediction (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: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/7/1708/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/7/1708/ (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:17:y:2024:i:7:p:1708-:d:1369336

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 ().