Foam-assisted oil recovery: A physics-based perspective

被引:1
|
作者
Ritacco, Hernan A. [1 ]
机构
[1] Univ Nacl UNS, Dept Fis, Inst Fis Sur IFISUR, CONICET, Ave L N Alem 1253 B8000CPB, Bahia Blanca, Argentina
关键词
Foams; Smart foams; Enhance oil recovery; EOR; Polyelectrolytes; Surfactants; Nanoparticles; Machine learning; Porous media; DYNAMIC SURFACE-TENSION; AQUEOUS FOAMS; ADSORPTION; RHEOLOGY; COALESCENCE; MECHANISMS; EMULSIONS; KINETICS; FILMS; FLOW;
D O I
10.1016/j.cocis.2024.101809
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
In this paper, I delve into the physics of foams within the context of Enhanced Oil Recovery (EOR). Foams present a promising prospect for use in EOR, applicable to both conventional and non-conventional oil wells. A primary challenge faced by oil industry technologists is ensuring foam stability in porous media under harsh conditions of temperature, pressure, and salinity. To surmount these challenges, a profound understanding of the physicochemical mechanisms governing foam formation and stability at a microscopic level is required. In this article, I explore some fundamental aspects of foam physics that should be considered when developing foam systems for EOR. I conclude the paper by briefly discussing the use of machine learning in the design of foam-assisted EOR, and by highlighting the potential of smart foams in the oil industry.
引用
收藏
页数:15
相关论文
共 50 条
  • [41] Physics-based circuits and systems
    Ohta, Jun
    Sugawa, Shigetoshi
    Shibata, Tadashi
    JAPANESE JOURNAL OF APPLIED PHYSICS, 2018, 57 (10)
  • [42] Physics-based visual understanding
    Massachusetts Inst of Technology, Cambridge, United States
    Comput Vision Image Undersanding, 2 (192-205):
  • [43] Physics-based models of evolution
    Kuntz, Irwin
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2010, 239
  • [44] Physics-Based Feature Engineering
    Jalali, Bahram
    Suthar, Madhuri
    Asghari, Mohammad
    Mahjoubfar, Ata
    OPTICS, PHOTONICS AND LASER TECHNOLOGY 2017, 2019, 222 : 255 - 275
  • [45] Physics-Based Channel Modeling for IRS-Assisted mmWave Communication Systems
    Lian, Zhuxian
    Zhang, Wendi
    Wang, Yajun
    Su, Yinjie
    Zhang, Bibo
    Jin, Biao
    Wang, Biao
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2024, 72 (05) : 2687 - 2700
  • [46] Analysis of Doherty Power Amplifier Matching Assisted by Physics-Based Device Modelling
    Guerrieri, Simona Donati
    Catoggio, Eva
    Bonani, Fabrizio
    ELECTRONICS, 2023, 12 (09)
  • [47] Perspective and comparative analysis of physics-based models for sodium-ion batteries
    Garapati, Vamsi Krishna
    Huld, Frederik
    Lee, Hanho
    Lamb, Jacob Joseph
    ELECTROCHIMICA ACTA, 2025, 514
  • [48] New opportunities in the paper and nonwovens industries with foam-assisted web forming and chemical application
    Oksanen, Antti
    Hjelt, Tuomo
    Lehmonen, Jani
    Rantanen, Timo
    Asikainen, Jaakko
    Salminen, Kristian
    TAPPI JOURNAL, 2023, 22 (01): : 61 - 66
  • [49] RIME: A physics-based optimization
    Su, Hang
    Zhao, Dong
    Heidari, Ali Asghar
    Liu, Lei
    Zhang, Xiaoqin
    Mafarja, Majdi
    Chen, Huiling
    NEUROCOMPUTING, 2023, 532 : 183 - 214
  • [50] Physics-Based Combustion Simulation
    Nielsen, Michael B.
    Bojsen-Hansen, Morten
    Stamatelos, Konstantinos
    Bridson, Robert
    ACM TRANSACTIONS ON GRAPHICS, 2022, 41 (05):