Regular Article

Efficient estimation of hydrolyzed polyacrylamide (HPAM) solution viscosity for enhanced oil recovery process by polymer flooding

¹ Department of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran
² Young Researchers and Elites Club, Meybod Branch, Islamic Azad University, Meybod, Iran
³ Young Researchers and Elites Club, Ahwaz Branch, Islamic Azad University, Ahwaz, Iran
⁴ Discipline of Chemical Engineering, School of Engineering, University of KwaZulu-Natal, Howard College Campus, King George V Avenue, Durban 4041, South Africa

^* Corresponding authors: amir_h_mohammadi@yahoo.com (A. H. Mohammadi) & e-mail: alireza.rostami.put2014@gmail.com (A. Rostami)

Received: 18 September 2017
Accepted: 22 February 2018

Abstract

Polymers applications have been progressively increased in sciences and engineering including chemistry, pharmacology science, and chemical and petroleum engineering due to their attractive properties. Amongst the all types of polymers, partially Hydrolyzed Polyacrylamide (HPAM) is one of the widely used polymers especially in chemistry, and chemical and petroleum engineering. Capability of solution viscosity increment of HPAM is the key parameter in its successful applications; thus, the viscosity of HPAM solution must be determined in any study. Experimental measurement of HPAM solution viscosity is time-consuming and can be expensive for elevated conditions of temperatures and pressures, which is not desirable for engineering computations. In this communication, Multilayer Perceptron neural network (MLP), Least Squares Support Vector Machine approach optimized with Coupled Simulated Annealing (CSA-LSSVM), Radial Basis Function neural network optimized with Genetic Algorithm (GA-RBF), Adaptive Neuro Fuzzy Inference System coupled with Conjugate Hybrid Particle Swarm Optimization (CHPSO-ANFIS) approach, and Committee Machine Intelligent System (CMIS) were used to model the viscosity of HPAM solutions. Then, the accuracy and reliability of the developed models in this study were investigated through graphical and statistical analyses, trend prediction capability, outlier detection, and sensitivity analysis. As a result, it has been found that the MLP and CMIS models give the most reliable results with determination coefficients (R²) more than 0.98 and Average Absolute Relative Deviations (AARD) less than 4.0%. Finally, the suggested models in this study can be applied for efficient estimation of aqueous solutions of HPAM polymer in simulation of polymer flooding into oil reservoirs.