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Artificial neural network framework for the selection of deep eutectic solvents promoted enhanced oil recovery by interfacial tension reduction mechanism

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  • Prasad, Tanishq
  • Raj, Saurav
  • Kundu, Debashis

Abstract

Estimation of interfacial tension (IFT) and % reduction in IFT of Brazil and China regions’ heavy crude oil is employed by Artificial Intelligence (AI) led frameworks. The uniqueness of proposed approach lies in using AI frameworks that are constructed using Artificial Neural Network (ANN) and meta-heuristic algorithms i.e. Genetic Algorithm (GA) and Grey Wolf Optimization (GWO) forms three AI frameworks namely ANN, ANN-GA, and ANN-GWO is employed for IFT estimation of heavy oil and deep eutectic solvent-brine interface. The hyper-parameters of the frameworks are trained, validated, and tested over 18228 IFT data points obtained from the Omani heavy crude oil. The intermolecular interactions between oil, brine, and DES are obtained by generating surface segments in COnductor like Screening MOdel for Real Solvent (COSMO-RS) framework. The composition of brine, mole ratio of oil, DES, and temperature are other input parameters for AI model. The IFT estimation leads to the selection of 3 hydrophilic DESs capable of greater than 75 % IFT reduction and 14 hydrophilic DESs capable of 50–75 % IFT reduction. ANN-GWO scores best AI framework among 3 whereas ANN-GA fails to estimate IFT reduction greater than 50 %. Analysis of IFT with respect to mole fraction reveals the most suitable range of DES addition for IFT reduction.

Suggested Citation

  • Prasad, Tanishq & Raj, Saurav & Kundu, Debashis, 2024. "Artificial neural network framework for the selection of deep eutectic solvents promoted enhanced oil recovery by interfacial tension reduction mechanism," Energy, Elsevier, vol. 307(C).
    • Handle: RePEc:eee:energy:v:307:y:2024:i:c:s0360544224023764
    DOI: 10.1016/j.energy.2024.132602
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    References listed on IDEAS

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    1. Olajire, Abass A., 2014. "Review of ASP EOR (alkaline surfactant polymer enhanced oil recovery) technology in the petroleum industry: Prospects and challenges," Energy, Elsevier, vol. 77(C), pages 963-982.
    2. Sahraei, Mohammad Ali & Çodur, Merve Kayaci, 2022. "Prediction of transportation energy demand by novel hybrid meta-heuristic ANN," Energy, Elsevier, vol. 249(C).
    3. Chaturvedi, Krishna Raghav & Narukulla, Ramesh & Amani, Mahmood & Sharma, Tushar, 2021. "Experimental investigations to evaluate surfactant role on absorption capacity of nanofluid for CO2 utilization in sustainable crude mobilization," Energy, Elsevier, vol. 225(C).
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