Enhanced oil recovery by nonionic surfactants considering micellization, surface, and foaming properties

Surfactants for enhanced oil recovery are important to study due to their special characteristics like foam generation,lowering interfacial tension between oleic and aqueous phases,and wettability alteration of reservoir rock surfaces.Foam is a good mobility control agent in enhanced oil recovery for improving the mobility ratio.In the present work,the foaming behavior of three nonionic ethoxylated surfactants,namely Tergitol 15-S-7,Tergitol 15-S-9,and Tergitol 15-S-12,was studied experimentally.Among the surfactants,Tergitol 15-S-12 shows the highest foamability.The effect of Na Cl concentration and synthetic seawater on foaming behavior of the surfactants was investigated by the test-tube shaking method.The critical micelle concentrations of aqueous solutions of the different nonionic surfactants were measured at 300 K.It was found that the critical micelle concentrations of all surfactants also increased with increasing ethylene oxide number.Dynamic light scattering experiments were performed to investigate the micelle sizes of the surfactants at their respective critical micelle concentrations.Core flooding experiments were carried out in sand packs using the surfactant solutions.It was found tha t22% additional oil was recovered in the case of all the surfactants over secondary water flooding.Tergitol 15-S-12exhibited the maximum additional oil recovery which is more than 26%after water injection.

Investigations on the relationship among the porosity,permeability and pore throat size of transition zone samples in carbonate reservoirs using multiple regression analysis,artificial neural network and adaptive neuro-fuzzy interface system

Finding an accurate method for estimating permeability aside from well logs has been a difficult task for many years.The most commonly used methods targeted towards regression technique to understand the correlation between pore throat radii,porosity and permeability are Winland and Pittman equation approaches.While these methods are very common among petrophysicists,they do not give a good prediction in certain cases.Consequently,this paper investigates the relationship among porosity,permeability,and pore throat radii using three methods such as multiple regression analysis,artificial neural network(ANN),and adaptive neuro-fuzzy inference system(ANFIS)for application in transition zone permeability modeling.Firstly,a comprehensive mercury injection capillary pressure(MICP)test was conducted using 228 transition zone carbonate core samples from a field located in the Middle-East region.Multiple regression analysis was later performed to estimate the permeability using pore throat and porosity measurement.For the ANN,a two-layer feed-forward neural network with sigmoid hidden neurons and a linear output neuron was used.The technique involves training,validation,and testing of input/output data.However,for the ANFIS method,a hybrid optimization consisting of least-square and backpropagation gradient descent methods with a subtractive clustering technique was used.The ANFIS combines both the artificial neural network and fuzzy logic inference system(FIS)for the training,validation,and testing of input/output data.The results show that the best correlation for the multiple regression technique is achieved for pore throat radii with 35%mercury saturation(R35).However,for both the ANN and ANFIS techniques,pore throat radii with 55%mercury saturation(R55)gives the best result.Both the ANN and ANFIS are later found to be more effective and efficient and thus recommended as compared with the multiple regression technique commonly used in the industry.