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Nanofluids and low-salinity water (LSW) flooding are two novel techniques for enhanced oil recovery. Despite some efforts on investigating benefits of each method, the pros and cons of their combined application need to be evaluated. This work sheds light on performance of LSW augmented with nanoparticles through examining wettability alteration and the amount of incremental oil recovery during the displacement process. To this end, nanofluids were prepared by dispersing silica nanoparticles (0.1 wt%, 0.25 wt%, 0.5 wt% and 0.75 wt%) in 2, 10, 20 and 100 times diluted samples of Persian Gulf seawater. Contact angle measurements revealed a crucial role of temperature, where no wettability alteration occurred up to 80 °C. Also, an optimum wettability state (with contact angle 22°) was detected with a 20 times diluted sample of seawater augmented with 0.25 wt% silica nanoparticles. Also, extreme dilution (herein 100 times) will be of no significance. Throughout micromodel flooding, it was found that in an oil-wet condition, a combination of silica nanoparticles dispersed in 20 times diluted brine had the highest displacement efficiency compared to silica nanofluids prepared with deionized water. Finally, by comparing oil recoveries in both water-and oil-wet micromodels, it was concluded that nanoparticles could enhance applicability of LSW via strengthening wettability alteration toward a favorable state and improving the sweep efficiency.

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Solubility is one of the most indispensable physicochemical properties determining the compatibility of components of a blending system. Research has been focused on the solubility of carbon dioxide in polymers as a significant application of green chemistry. To replace costly and time‐consuming experiments, a novel solubility prediction model based on a decision tree, called the stochastic gradient boosting algorithm, was proposed to predict CO2 solubility in 13 different polymers, based on 515 published experimental data lines. The results indicate that the proposed ensemble model is an effective method for predicting the CO2 solubility in various polymers, with highly satisfactory performance and high efficiency. It produces more accurate outputs than other methods such as machine learning schemes and an equation of state approach.