Increasing concentrations of greenhouse gases (GHGs) such as CO2 in the atmosphere is a global warming. Human activities are a major cause of increased CO2 concentration in atmosphere, as in recent decade, two-third of greenhouse effect was caused by human activities. Carbon capture and storage (CCS) is a major strategy that can be used to reduce GHGs emission. There are three methods for CCS: pre-combustion capture, oxy-fuel process, and post-combustion capture. Among them, post-combustion capture is the most important one because it offers flexibility and it can be easily added to the operational units. Various technologies are used for CO2 capture, some of them include: absorption, adsorption, cryogenic distillation, and membrane separation. In this paper, various technologies for post-combustion are compared and the best condition for using each technology is identified.

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In this study, two types of plants based natural cationic surfactants, named Mulberry and Henna are introduced and the application of these natural surfactants in wettability alteration of reservoir rock and reducing the interfacial tension of water-oil system is investigated. For this purpose, two natural-based surfactants were extracted from the leaves of the trees of addressed plants and then the interfacial tension (IFT) values between oil and natural surfactant solution and also the contact angle values between natural surfactant solution and rock sample were measured. The results demonstrated that Mulberry extract was able to lower the interfacial tension between oil and distilled water from 43.9 to 4.01 mN/m, while Henna extract could reduce the IFT from 43.9 to 3.05 mN/m. These natural surfactants were also able to reduce the contact angle of rock/fluid system which shows the wettability is altering to water wet system and so it may increase recovery factor by reducing residual oil saturation and Henna extract could reduce the contact angle more than that of Mulberry leaf extract. According to these results in addition to the low price of generating natural surfactants, the feasibility of using these kinds of surfactants in future oil recovery processes is of major concern.

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Injection of carbon dioxide is a familiar, cost-effective and influential technology of enhancing oil recovery whose application has been limited owing to the low n-alkane solubility in supercritical CO2. Thus, determining the amount of dissolved n-alkane in supercritical CO2 is of importance. Accordingly, in this study, least-squares support vector machine (LSSVM), tuned with two different optimizing algorithms, namely particle swarm optimization (PSO) and cross-validation-assisted Simplex algorithm (CV-Simplex), has been used for this simulation process. Based on the results, the predicted values for dissolved n-alkane mole fraction in supercritical CO2 by PSO–LSSVM model were quite in line with experimental data. Furthermore, the accuracy of these models was compared with Chrastil correlation. Absolute average relative error for PSO–LSSVM, CV-Simplex–LSSVM and Chrastil was calculated to be 3.88%, 13.49% and 18.22% for total dataset, respectively, which leaves PSO–LSSVM as the superior model with the highest accuracy. Finally, the statistical parameters of absolute average relative error, mean square error and determination coefficient equal to 3.88%, 0.0164 and 0.994 for total dataset, respectively, proved that PSO–LSSVM model is an efficient method that can predict n-alkane solubility in supercritical CO2 with high precision within 8.99–45.90 MPa pressure and 308.15–344.15 K temperature range.