Increasing the viscosity of injected water by the addition of polymer improves the displacement efficiency during the water flooding process. In this study, a sulfonated polyacrylamide copolymer has been added to salt water. Several parameters, such as polymer concentration, shear rate, NaCl concentration, molecular weight and sulfonation degree, have a significant effect on the polymer solution viscosity. The main objective of this paper is to investigate how the polymer solution viscosity varies with changes in the input parameters so as to identify the relative importance of these parameters. This paper incorporates the Design of Experiments technique using Taguchi's method and the Analysis of Variance (ANOVA) to investigate the effect of process variables on the viscosity of a polymer solution. Five input parameters and six possible interactions have been investigated. The analysis of the experimental results revealed that two input parameters, namely, polymer concentration and shear rate, have the most significant impact on polymer viscosity. Two strong interactions were observed in the (1) NaCl concentration and sulfonation degree and (2) molecular weight and NaCl concentration studies. The results show that the Taguchi method was successful in identifying the main effects and interaction effects. ANOVA further buttresses the results from Taguchi's method by showing a strong similarity in its results.

This study has utilized the response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) approaches for the modeling of polymer solution viscosity. In the absence of reports in the previous study on applying these two approaches, the main objective of this study has been to compare the performance of these methods toward the viscosity modeling of a polymer solution. By utilizing RSM technique, the effects of three independent parameters including shear rate, polymer concentration, and sodium chloride concentration on viscosity of polymer solution were examined. The RSM results showed that all the parameters were not equally important in the polymer solution viscosity. Moreover, analysis of variance (ANOVA) was also carried out and indicated that there was no evidence of lack of fit in the RSM model. As a second approach for polymer solution viscosity modeling, ANFIS was utilized with two rules constructed based on the first-order Sugeno fuzzy approach and trained by back propagation neural networks algorithm. High coefficient of determination (R 2 ) values ( [99%) showed that the prediction ability of both the ANFIS and RSM models was good enough for the response when the interpolation ability of the models was considered. In order to evaluate the extrapolation abilities of the two developed models, two data sets lying beyond the originally considered data were also taken into account. The results showed that their extrapolation predictive ability was poor. The reason could simply be the inherent behavior of the polymeric solution, i.e., the correlational structure seen in the sample used in the training step did not continue outside the sample space.

A polymer flooding technique is developed to reduce the amount of residual oil saturation that cannot be recovered through waterflooding or gas injection processes. Using polymer flooding in the case of high-viscosity oil has been successful due to reducing mobility ratio (M), whereas there is conflict in efficiency of polymer flooding in the case of low-viscosity oil.In this study, to investigate the behavior of polymer flooding in low-viscosity oil, the transparent materials (glass) were used to construct a micromodel and to study various aspects of micro-displacement. By using a micromodel, the displacement of the fluid and menisci was observed and investigated with the aid of images captured by a camera.In this work, two kinds of quarter five-spot glass micromodel patterns were designed and developed and considered as pores medium. These patterns were saturated with light and low-viscosity oil samples from an Iranian fractured reservoir and then flooded by a polymer slug in low-pressure and low-temperature conditions. Three polymer types, hydrolyzed polyacrylamide (HPAM 25%), very low hydrolyzed polyacrylamide (<5%; PA) and xanthan, were utilized to inspect the effects of polymer type, concentration, injection rate, pore structure, and presence of connate water on recovery of low-viscosity oil.The results of experiments illustrated that recovery from HPAM is higher than both xanthan and very low hydrolyzed polyacrylamide. Rate sensitivity tests showed that increasing rate injection induced a decrease in recovery. Flooding in two different micromodel patterns demonstrated that higher permeability leads to lower recovery. In addition, experiments indicated that the presence of connate water caused a reduction in recovery. Finally, compared with waterflooding, polymer flooding resulted in a considerable growth in ultimate oil recovery.

A surfactant flooding technique is developed to reduce the amount of residual oil saturation by reducing interfacial tension between multiple phases. Using surfactant flooding in the case of high viscosity oil has been successful, whereas there has not been a complete study of efficiency of this method when dealing with low viscosity oil. In the present study, to investigate the behavior of surfactant flooding in low viscosity oil, the transparent material (glass) was used to construct a micromodel and to study various features of micro-displacement. By implementing a micromodel, the displacement of the fluid and menisci was observed and investigated with the aid of high resolution images. In this work, three types of quarter fivespot glass micromodel patterns were designed and developed, and considered as porous medium. These patterns were saturated with a light and low viscosity oil sample from an Iranian fractured reservoir, and then flooded by surfactant slug in low pressure and low temperature conditions. Two surfactant types, PNX-2360 TM and linear alkylbenzene sulphonate, were utilized to inspect the effects of surfactant type, surfactant concentration, co-surfactant concentration, and pore structure in the presence of connate water, and their results were compared to water flooding. Moreover, high resolution images were captured, which illustrate the interaction between surfactant and connate water in micro-scale. The results of experiments illustrated that linear alkylbenzene sulphonate results in higher recovery. It was also found that recovery rises by increasing surfactant concentration. Testing different concentrations of co-surfactants demonstrated that, however, lower concentration of co-surfactants leads to higher recovery, but this increase in recovery is economically considerable. In addition, compared with water flooding, surfactant flooding resulted in a considerable growth in ultimate oil recovery.