With a constant upsurge in energy demand, production from depleted and harsh reservoirs through enhanced oil recovery techniques (EOR) has significantly increased. Among many EOR techniques, chemical EOR (cEOR) is one of the most widely used methods of oil extraction. Surfactants used in cEOR are instrumental in reducing interfacial tension (IFT) and altering the wettability of rock, which leads to additional oil recovery. This review draws attention to detail on surfactants from fundamentals to field scale. Properties of surfactants like phase behaviors, critical micelle concentration (CMC), hydrophilic–lipophilic balance and deviation, zeta potential, and their importance are discussed in depth. The presence of a saline environment, polymer, cosurfactant, and other factors affecting the performance of surfactant during the cEOR process are also elaborated. Key findings on surfactant adsorption on reservoir rock with other influencing aspects have also been reported in this study. Types of surfactants, from basic to the likes of polymeric, viscoelastic, Gemini, natural, and their effects on oil recoveries have been analyzed and compared. Special emphasis on emerging aids for surfactant flooding such as applications of nanotechnology, use amphoteric Janus particles, and synergies of surfactant–low salinity water flooding, along with their mechanisms and recent advances have been thoroughly duscussed. Lastly, the review delineates discerning criteria for the selection of surfactants, reviews recent field applications, and outlines the challenges that the industry faces while implementing surfactant cEOR. It has been found that exhaustive studies have been conducted on sandstones with success. However, extreme temperature and saline conditions in the case of carbonate reservoirs limit the applicability of surfactants, and the pursuit to accomplish its efficacy continues.

The increase in hydrocarbon production from problematic production zones having high fluid loss and formation damage has led to the emergence of non-damaging drilling fluids (NDDF). Recently, nanotechnology has found a wide array of applications in the oil and gas industry. Most applications of nanotechnology and enhancement in properties of drilling fluids are restricted to bentonite, xanthan gum and a few oil-based mud. In this study, the effects of silica and copper oxide nanoparticles on polyamine-based NDDF and conventional bentonite-based drilling fluids (BDF) were investigated. Silica nanoparticles were prepared using sol-gel method, and copper oxide nanoparticles were synthesized using co-precipitation method. Nano-based drilling fluids were prepared by dispersing nanoparticles in concentrations of 0.5%, 0.8% and 1% by weight. Furthermore, testing of these nano-based drilling fluids was conducted by measuring specific gravity, pH, rheological properties and filtrate loss at surface temperature (room temperature) and then aging it at bottom-hole temperature (80 °C). The addition of silica and copper oxide nanoparticles to both the drilling fluids did not show much effect on pH and specific gravity. Addition of 0.5% concentration of silica nanoparticles in NDDF showed least degradation in rheological properties compared to other fluids. It showed reduction in filtrate loss by 31%. Moreover, silica nanoparticles in conjunction with BDF acted as a mud thinner showing a decrease in viscosity and yield point. On the contrary, when used with NDDFs, silica nanoparticles acted as a mud thickener. Copper oxide nanoparticles behaved as a thinner in both the drilling fluids with a highest reduction in plastic viscosity of 24% for 0.8% of copper oxide nanoparticle in BDF. Thinning properties were enhanced as the doping concentrations of copper oxide nanoparticles increased; however, the fluid loss controlling ability decreased except for 0.5% concentration by 31% and 24% when used with both the drilling fluids. Additionally, optimal Herschel-Bulkley parameters have been determined by using genetic algorithm to minimize the function of sum of squared errors between observed values and model equation.

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