Application of microfluidics in chemical enhanced oil recovery: A review. (1st May 2022)

• Record Type: Journal Article
• Authors: Fani, Mahmood; Pourafshary, Peyman; Mostaghimi, Peyman; Mosavat, Nader
• Is Part Of: Fuel. Volume 315(2022)
• Publication Date: 2022-05-01
• Subjects: Microfluidics -- Enhanced oil recovery -- Chemical enhanced oil recovery -- Pore-scale visualization -- Lab on a chip; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2022.123225
• Languages: English
• ISSNs: 0016-2361

Abstract

Graphical abstract: Highlights: Less-known enhanced oil recovery mechanisms in microscale are probed. Challenges in detecting the efficient chemical enhanced oil recovery method are explained. Researchable schemes and mechanisms for chemical flooding are evaluated at the pore scale. Future trends and advanced technologies are scrutinized for applications of microfluidics for enhanced oil recovery. Abstract: In Chemical Enhanced Oil Recovery (CEOR), various chemicals such as polymer, surfactant, alkaline, and nanoparticles are injected solely or in combination to mobilize the remaining oil. Wettability alteration, interfacial tension reduction, modification of mobility ratio, stabilizing the injection fluid, degradation, swelling, adsorption, channel blockage, plugging, fluid diversion, and emulsification are the most important phenomena. Microfluidic chips are the best tools to mimic and visualize these microscale processes. Therefore, there are vast potentials for microfluidics to shed light on the micro-scale aspect of CEOR. In this paper, the aim is to collect, analyze, and correlate the current knowledge and efforts and review them critically while discussing potential improvements for future research in the field of microfluidic for CEOR processes. There are certain limitations to the conventional CEOR experimentation, which can be addressed using microfluidic methods for fast, accurate, and reliable analyses. The versatility of the lab-on-a-chip devices and methods can assist users to simulate different heterogeneity and mineralogy patterns, apply automated image analysis through implementing machine learning algorithms, employing molecular tagging to locate and track the injection fluid front and various fluids or solids, and use multi-dimension chips to perform more realistic testing leading to better understating of fluid flow and the associated mechanisms in porous media.