SCI和EI收录∣中国化工学会会刊

中国化学工程学报 ›› 2025, Vol. 87 ›› Issue (11): 19-34.DOI: 10.1016/j.cjche.2025.05.005

• • 上一篇    下一篇

Machine learning-assisted characterization of oil micro-displacement hydrodynamics by bionanofluid-flooding in microchannel sand-packed porous media towards enhanced oil recovery

Zhihong Chen1, Jiawei Wu1, Wei Zhang1, Wenjing Zhang2, Xiaoling Lou1, Junxian Yun2   

  1. 1. State Key Laboratory of Green Chemistry Synthesis and Conversion Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China;
    2. State Key Laboratory of Advanced Separation Membrane Materials, Zhejiang Key Laboratory of Surface and Interface Science and Engineering for Catalysts, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
  • 收稿日期:2025-03-10 修回日期:2025-05-16 接受日期:2025-05-19 出版日期:2025-11-28 发布日期:2025-05-24
  • 通讯作者: Wei Zhang,E-mail:zhangwei@zjut.edu.cn;Junxian Yun,E-mail:yunjx@zjut.edu.cn
  • 基金资助:
    The authors gratefully acknowledge the financial supports partially by the National Natural Science Foundation of China (22078296, 21576240) and the Zhejiang Provincial Natural Science Foundation of China (LD21B060001).

Machine learning-assisted characterization of oil micro-displacement hydrodynamics by bionanofluid-flooding in microchannel sand-packed porous media towards enhanced oil recovery

Zhihong Chen1, Jiawei Wu1, Wei Zhang1, Wenjing Zhang2, Xiaoling Lou1, Junxian Yun2   

  1. 1. State Key Laboratory of Green Chemistry Synthesis and Conversion Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China;
    2. State Key Laboratory of Advanced Separation Membrane Materials, Zhejiang Key Laboratory of Surface and Interface Science and Engineering for Catalysts, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
  • Received:2025-03-10 Revised:2025-05-16 Accepted:2025-05-19 Online:2025-11-28 Published:2025-05-24
  • Contact: Wei Zhang,E-mail:zhangwei@zjut.edu.cn;Junxian Yun,E-mail:yunjx@zjut.edu.cn
  • Supported by:
    The authors gratefully acknowledge the financial supports partially by the National Natural Science Foundation of China (22078296, 21576240) and the Zhejiang Provincial Natural Science Foundation of China (LD21B060001).

摘要: The displacement of residual crude oil and enhanced oil recovery from reservoirs of mature oil fields are challenging worldwide and have received intensive attentions in oil and gas industry. In this work, a novel method for enhanced oil recovery by displacement of oil with bionanofluids was proposed. Micro-displacement hydrodynamics of crude oil in microchannel sand-packed porous media by the bionanofluid were investigated by high-speed imaging. The machine learning models with the extreme gradient boosting (XGBoost) algorithm was developed for the prediction of residual oil saturation during the micro-displacement processes. The residual oil droplets within the porous media after the waterflooding were effectively removed through bionanofluid-flooding, resulting in additional enhanced oil recovery of 39.0%, which is double the recovery achieved by waterflooding at the same displacement velocity. By wavelet-transform image enhancement and the XGBoost algorithm in the machine learning, the residual oil saturations along the porous media were predicted accurately with the mean squared errors of 0.0045 and 0.0030 in the waterflooding and the bionanofluid-flooding, respectively. The results indicated that the machine learning is effective in characterizing the displacement behaviors and the bionanofluid-flooding could be an interesting approach, and thus has potential applications in enhanced oil recovery of waterflooding reservoirs.

关键词: Microchannels, Two-phase flow, Biotechnology, Bionanofluid, Enhanced oil recovery, Machine learning

Abstract: The displacement of residual crude oil and enhanced oil recovery from reservoirs of mature oil fields are challenging worldwide and have received intensive attentions in oil and gas industry. In this work, a novel method for enhanced oil recovery by displacement of oil with bionanofluids was proposed. Micro-displacement hydrodynamics of crude oil in microchannel sand-packed porous media by the bionanofluid were investigated by high-speed imaging. The machine learning models with the extreme gradient boosting (XGBoost) algorithm was developed for the prediction of residual oil saturation during the micro-displacement processes. The residual oil droplets within the porous media after the waterflooding were effectively removed through bionanofluid-flooding, resulting in additional enhanced oil recovery of 39.0%, which is double the recovery achieved by waterflooding at the same displacement velocity. By wavelet-transform image enhancement and the XGBoost algorithm in the machine learning, the residual oil saturations along the porous media were predicted accurately with the mean squared errors of 0.0045 and 0.0030 in the waterflooding and the bionanofluid-flooding, respectively. The results indicated that the machine learning is effective in characterizing the displacement behaviors and the bionanofluid-flooding could be an interesting approach, and thus has potential applications in enhanced oil recovery of waterflooding reservoirs.

Key words: Microchannels, Two-phase flow, Biotechnology, Bionanofluid, Enhanced oil recovery, Machine learning