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<p>In recent years, nanotechnology has gone through great advancement because of its number of applications in the oil and gas sector. The usage of nanoparticles (NPs) flooding is considered the most effective method for oil recovery purposes. Despite its substantial benefits, the experimental studies on Enhanced oil recovery (EOR) require a lot of time, complicated expensive resources such as advanced equipment for experimental setup, and NPs. To overcome these issues, several mathematical models have been developed to predict oil recovery factors under different reservoir conditions, which are time and cost efficient. Therefore, the aim of this study is to explore various types of NPs and their respective roles in EOR, highlighting their impact on oil recovery factors, along with thermophysical properties of nanofluids (NFs) that improve oil displacement efficiency by reducing interfacial tension (IFT), altering wettability, and increasing fluid mobility based on the latest literature review. Additionally, this paper reviews and critically analyses the latest developments in mathematical modelling and simulating NF transport in porous media (PM), focusing on Darcy’s law, NP retention, and changes in reservoir properties. One section is dedicated to deliberating the future directions in the modelling approach, like the mathematical modelling of magnetohydrodynamics (MHD), electromagnetohydronamics (EMHD) as well as hybrid nanofluids (HNFs) and integration with AI for optimization to encourage the readers to contribute further to the advancement of modelling NF-assisted EOR processes.</p>

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Nanofluid Thermophysical Property Modeling for Enhanced Oil Recovery: A Comprehensive Review and Future Outlook for Artificial Intelligence Integration

  • 期刊论文

  • 发表日期：

    2025年9月11日

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  • Sanjay Kumar,
  • Hamzah Sakidin,
  • Mudasar Zafar,
  • Hazoor Bux Lanjwani,
  • Imran Mir Chohan

摘要

In recent years, nanotechnology has gone through great advancement because of its number of applications in the oil and gas sector. The usage of nanoparticles (NPs) flooding is considered the most effective method for oil recovery purposes. Despite its substantial benefits, the experimental studies on Enhanced oil recovery (EOR) require a lot of time, complicated expensive resources such as advanced equipment for experimental setup, and NPs. To overcome these issues, several mathematical models have been developed to predict oil recovery factors under different reservoir conditions, which are time and cost efficient. Therefore, the aim of this study is to explore various types of NPs and their respective roles in EOR, highlighting their impact on oil recovery factors, along with thermophysical properties of nanofluids (NFs) that improve oil displacement efficiency by reducing interfacial tension (IFT), altering wettability, and increasing fluid mobility based on the latest literature review. Additionally, this paper reviews and critically analyses the latest developments in mathematical modelling and simulating NF transport in porous media (PM), focusing on Darcy’s law, NP retention, and changes in reservoir properties. One section is dedicated to deliberating the future directions in the modelling approach, like the mathematical modelling of magnetohydrodynamics (MHD), electromagnetohydronamics (EMHD) as well as hybrid nanofluids (HNFs) and integration with AI for optimization to encourage the readers to contribute further to the advancement of modelling NF-assisted EOR processes.