Surfactant flooding is an Enhanced Oil Recovery technique used for decreasing oil trapping in pore spaces by reducing the interfacial tension (IFT) between oil and water. In this study, three types of plants based on natural cationic surfactants, named Olive, Spistan and Prosopis, are introduced and the application of these natural surfactants in reducing the interfacial tension of wateroil system is investigated. For this purpose, three naturalbased surfactants were extracted from the leaves of the trees of addressed plants and then the interfacial tension values between oil and natural surfactant solution were measured using the pendant drop method. The results demonstrated that Olive extract was able to lower the IFT between kerosene and distilled water from 36.5 to 14 mN/ m, while Spistan and Prosopis extract could reduce the IFT from 36.5 to 20.15 mN/m and 36.5 to 15.11 mN/m, respectively. 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.
In petroleum industries, nanofluids have the potential to improve the characteristics of the fluids used in drilling wells or Enhanced Oil Recovery (EOR) processes. In this study, a water based mud containing polymer was considered as the base fluid. Different concentrations of TiO2 nanoparticle (0, 0.5 and 0.75 wt%) and different concentrations of KCl salt (0, 0.5, 1.5, and 3 wt%) were added to the base fluid and exposed to different temperatures (30, 50, 70 and 90 °C) with 19 different shear rates for investigating the effects of nanoparticle concentration, salt concentration, temperature and shear rate on viscosity of the base mud. Presence of TiO2 particles enhanced not only the rheological behavior but also electrical and thermal conductivity of fluid up to 25% and 43%, respectively. Furthermore, the stability of the fluid containing salt and nanoparticle was investigated in these temperatures owing to the fact that the temperature could cause degradation of the fluid. For the purpose of investigating this phenomenon, the after cooling experiment was conducted. In addition, the data gathered in this investigation were examined by using three famous rheological models (Power law, Herschel-Bulkley and Herschel-Bulkley-Papanastasiou models) and the rheological parameters of each model were determined.
Three models were developed to estimate the potential of the selected bacteria Petrotoga sp., a thermophilic anaerobic oil‐degrading microorganism. Fourteen data sets of these bacteria were simulated by a multilayer feed‐forward neural network and an adaptive neuro‐fuzzy interference system. Twelve data sets served for training and two for testing these models. A simplified numerical model was performed assuming two phases in the growth process of oil‐degrading microorganisms, the logarithmic growth phase and the death phase. Comparison between these models in predicting bacterial cell concentration for different data sets indicates little difference between the overall average relative errors of the three methods and that all can be applied for prediction. Effects of salinity concentration, amount of yeast extract, and temperature on bacterial cell concentration were simulated by numerical and neural network models.
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