Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Ahmed F Belhaj; Khaled A Elraies; Mohamad S Alnarabiji; Firas A Abdul Kareem; Juhairi A Shuhli; Syed M Mahmood; Hadi Belhaj

doi:10.1016/j.cej.2020.127081

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Chem Eng J. 2021 Feb 15:406:127081. doi: 10.1016/j.cej.2020.127081. Epub 2020 Sep 23.

Authors

Ahmed F Belhaj¹, Khaled A Elraies¹, Mohamad S Alnarabiji^{1

2}, Firas A Abdul Kareem², Juhairi A Shuhli¹, Syed M Mahmood¹, Hadi Belhaj³

Affiliations

¹ Department of Petroleum Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia.
² Department of Chemical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia.
³ Department of Petroleum Engineering, Khalifa University of Science and Technology, Sas Al Nakhl Campus, P.O. BOX 2533, Abu Dhabi, United Arab Emirates.

Abstract

Throughout the application of enhanced oil recovery (EOR), surfactant adsorption is considered the leading constraint on both the successful implementation and economic viability of the process. In this study, a comprehensive investigation on the adsorption behaviour of nonionic and anionic individual surfactants; namely, alkyl polyglucoside (APG) and alkyl ether carboxylate (AEC) was performed using static adsorption experiments, isotherm modelling using (Langmuir, Freundlich, Sips, and Temkin models), adsorption simulation using a state-of-the-art method, binary mixture prediction using the modified extended Langmuir (MEL) model, and artificial neural network (ANN) prediction. Static adsorption experiments revealed higher adsorption capacity of APG as compared to AEC, with sips being the most fitted model with R² (0.9915 and 0.9926, for APG and AEC respectively). It was indicated that both monolayer and multilayer adsorption took place in a heterogeneous adsorption system with non-uniform surfactant molecules distribution, which was in remarkable agreement with the simulation results. The (APG/AEC) binary mixture prediction depicted contradictory results to the experimental individual behaviour, showing that AEC had more affinity to adsorb in competition with APG for the adsorption sites on the rock surface. The adopted ANN model showed good agreement with the experimental data and the simulated adsorption values for APG and AEC showed a decreasing trend as temperature increases. Simulating the impact of binary surfactant adsorption can provide a tremendous advantage of demonstrating the binary system behaviour with less experimental data. The utilization of ANN for such prediction procedure can minimize the experimental time, operating cost and give feasible predictions compared to other computational methods. The integrated workflow followed in this study is quite innovative as it has not been employed before for surfactant adsorption studies.

Keywords: Adsorption isotherm; Computational chemistry; Critical micelle concentration; HPLC; Harsh reservoir conditions; Modified extended Langmuir model.