TY - JOUR
T1 - Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application
AU - Belhaj, Ahmed F.
AU - Elraies, Khaled A.
AU - Alnarabiji, Mohamad S.
AU - Abdul Kareem, Firas A.
AU - Shuhli, Juhairi A.
AU - Mahmood, Syed M.
AU - Belhaj, Hadi
N1 - Funding Information:
Authors would like to acknowledge Universiti Teknologi PETRONAS (UTP) for funding this work under Grant No. ( 015LC0-137 ). Special thanks and appreciation go to the Centre of Research in Enhanced Oil Recovery (COR-EOR) in UTP, for providing laboratory facilities and technical support.
Funding Information:
Authors would like to acknowledge Universiti Teknologi PETRONAS (UTP) for funding this work under Grant No. (015LC0-137). Special thanks and appreciation go to the Centre of Research in Enhanced Oil Recovery (COR-EOR) in UTP, for providing laboratory facilities and technical support.
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021/2/15
Y1 - 2021/2/15
N2 - 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.
AB - 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.
KW - Adsorption isotherm
KW - Computational chemistry
KW - Critical micelle concentration
KW - Harsh reservoir conditions
KW - HPLC
KW - Modified extended Langmuir model
UR - http://www.scopus.com/inward/record.url?scp=85092146621&partnerID=8YFLogxK
U2 - 10.1016/j.cej.2020.127081
DO - 10.1016/j.cej.2020.127081
M3 - Article
AN - SCOPUS:85092146621
SN - 1385-8947
VL - 406
JO - Chemical Engineering Journal
JF - Chemical Engineering Journal
M1 - 127081
ER -