Bui, T. et al. Water/oil interfacial tension reductionAn interfacial entropy driven process. JPCCP 23(44), 2507525085 (2021).

ADS Google Scholar

Kalam, S., Khan, M. R., Shakeel, M., Mahmoud, M. & Abu-khamsin, S. Smart Algorithms for Determination of Interfacial Tension (IFT) Between Injected Gas and Crude Oil-Applicable to EOR Projects (Middle East Oil, Gas and Geosciences Show/OnePetro, 2023).

Book Google Scholar

Garmsiri, H. et al. Stability of the emulsion during the injection of anionic and cationic surfactants in the presence of various salts. Sci. Rep. 13(1), 11337 (2023).

Article ADS PubMed PubMed Central Google Scholar

Shafiei, M., Kazemzadeh, Y., Martyushev, D. A., Dai, Z. & Riazi, M. Effect of chemicals on the phase and viscosity behavior of water in oil emulsions. Sci. Rep. 13(1), 4100 (2023).

Article ADS PubMed PubMed Central Google Scholar

Kalatehno, J. M. & Khamehchi, E. A novel packer fluid for completing HP/HT oil and gas wells. J. Petrol. Sci. Eng. 203, 108538 (2021).

Article Google Scholar

Drexler, S., Hoerlle, F., Godoy, W., Boyd, A. & Couto, P. Wettability alteration by carbonated brine injection and its impact on pore-scale multiphase flow for carbon capture and storage and enhanced oil recovery in a carbonate reservoir. Appl. Sci. 10(18), 6496 (2020).

Article Google Scholar

Hamidpour, S., Safaei, A., Kazemzadeh, Y., Hasan-Zadeh, A. & Khormali, A. Calculation of IFT in porous media in the presence of different gas and normal alkanes using the modified EoS. Sci. Rep. 13(1), 8077 (2023).

Article ADS PubMed PubMed Central Google Scholar

Kalatehno, J. M., Khamehchi, E., Abbasi, A. & Khaleghi, M. R. A novel approach to determining appropriate additive concentrations for stimulation of gas carbonate reservoirs. Results Eng. 20, 101440 (2023).

Article Google Scholar

Hou, X. & Sheng, J. J. Experimental study on the effects of IFT reduction and shut-in on water blockage after hydraulic fracturing in tight sandstone reservoirs based on the NMR method. Energy Fuels. 37(9), 65696584 (2023).

Article Google Scholar

Pereira, L. M., Chapoy, A., Burgass, R. & Tohidi, B. Interfacial tension of CO2+ brine systems. Exp. Predict. Model. 103, 6475 (2017).

Google Scholar

Kim, B. et al. Ensemble machine learning-based approach for predicting of FRP-concrete interfacial bonding. Mathematics 10(2), 231 (2022).

Article Google Scholar

Tadros, T. Gibbs adsorption isotherm. In Encyclopedia of Colloid and Interface Science (Tadros, T. ed.). 626 (Springer, 2013).

Sibanda, D., Oyinbo, S. T. & Jen, T.-C. A review of atomic layer deposition modelling and simulation methodologies: Density functional theory and molecular dynamics. Nanotechnol. Rev. 11(1), 13321363 (2022).

Article Google Scholar

Singh, S. K., Chaurasia, A. & Verma, A. Basics of Density Functional Theory, Molecular Dynamics, and Monte Carlo Simulation Techniques in Materials Science. In Coating Materials: Computational Aspects, Applications and Challenges (eds Verma, A. et al.) 111124 (Springer, 2023).

Chapter Google Scholar

Zhao, X., Duan, W., Zeng, X. & Liu, Y. J. Measurements of surface tension of R1234yf and R1234ze (E). Int. J. Refrig. 63(1), 2126 (2018).

Google Scholar

Clegg, C. Contact Angle Made Easy: Carl Clegg (2013).

DA. Standard Test Methods for Surface and Interfacial Tension of Solutions of Paints, Solvents, Solutions of Surface-Active Agents, and Related Materials. Annual Book of ASTM Standards. (American Society for Testing and Materials, 2014).

Gupta, A., Pandey, A., Kesarwani, H., Sharma, S. & Saxena, A. Automated determination of interfacial tension and contact angle using computer vision for oil field applications. J. Petrol. Explor. Prod. Technol. 12(5), 14531461 (2022).

Article Google Scholar

Esteghlal, S., Samadi, S. H., Hosseini, S. M. H. & Moosavi-Movahedi, A. A. Identification of machine learning neural-network techniques for prediction of interfacial tension reduction by zein based colloidal particles. Ind. Eng. Chem. Res. 36, 106546 (2023).

Google Scholar

Dargi, M., Khamehchi, E. & Mahdavi, K. J. Optimizing acidizing design and effectiveness assessment with machine learning for predicting post-acidizing permeability. Sci. Rep. 13(1), 11851 (2023).

Article ADS PubMed PubMed Central Google Scholar

Zamani, M. G., Nikoo, M. R., Rastad, D. & Nematollahi, B. A comparative study of data-driven models for runoff, sediment, and nitrate forecasting. J. Environ. Manag. 341, 118006 (2023).

Article Google Scholar

Khamehchi, E., Dargi, M., Imeri, M., Kalatehno, J.M. & Khaleghi, M.R. Pipe Diameter Optimization and Two-Phase Flow Pressure Drop in Seabed Pipelines: A Genetic Algorithm Approach.

Ahmadi, M. A. & Mahmoudi, B. Development of robust model to estimate gasoil interfacial tension using least square support vector machine: Experimental and modeling study. J. Supercrit. Fluids 107, 122128 (2016).

Article Google Scholar

Andersson, M., Eckert, F., Reinisch, J. & Klamt, A. Prediction of aliphatic and aromatic oilwater interfacial tension at temperatures > 100 C using COSMO-RS. Fluid Phase Equilib. 476, 2529 (2018).

Article Google Scholar

Amar, M. N., Shateri, M., Hemmati-Sarapardeh, A. & Alamatsaz, A. Modeling oil-brine interfacial tension at high pressure and high salinity conditions. J. Petrol. Sci. Eng. 183, 106413 (2019).

Article Google Scholar

Dehaghani, A. H. S. & Soleimani, R. Estimation of interfacial tension for geological CO2 storage. Chem. Eng. Technol. 42(3), 680689 (2019).

Article Google Scholar

Kirch, A., Celaschi, Y. M., de Almeida, J. M. & Miranda, C. R. Brineoil interfacial tension modeling: Assessment of machine learning techniques combined with molecular dynamics. ACS Appl. Mater. Interfaces 12(13), 1583715843 (2020).

Article PubMed Google Scholar

Zhang, J., Feng, Q. & Zhang, X. (eds.) The use of machine learning methods for fast estimation of CO2-brine interfacial tension: A comparative study. In Proceedings of the 2020 5th International Conference on Machine Learning Technologies (2020).

Amar, M. N. Towards improved genetic programming based-correlations for predicting the interfacial tension of the systems pure/impure CO2-brine. J. Taiwan Inst. Chem. Eng. 127, 186196 (2021).

Article Google Scholar

Cui, Z. & Li, H. Toward accurate density and interfacial tension modeling for carbon dioxide/water mixtures. Petrol. Sci. 18, 509529 (2021).

Article Google Scholar

Setiawan, R., Daneshfar, R., Rezvanjou, O., Ashoori, S. & Naseri, M. Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artificial intelligence. Environ. Dev. Sustain. 23, 1760617627 (2021).

Article Google Scholar

Bui, T. et al. Water/oil interfacial tension reductionAn interfacial entropy driven process. Phys. Chem. Chem. Phys. 23(44), 2507525085 (2021).

Article PubMed Google Scholar

Yang, Y., Che Ruslan, M. F. A., Narayanan Nair, A. K., Qiao, R. & Sun, S. Interfacial properties of the hexane+ carbon dioxide+ water system in the presence of hydrophilic silica. J. Chem. Phys. 157(23), 37 (2022).

Article Google Scholar

Seddon, D., Mller, E. A. & Cabral, J. T. Machine learning hybrid approach for the prediction of surface tension profiles of hydrocarbon surfactants in aqueous solution. J. Colloid Interface Sci. 625, 328339 (2022).

Article ADS PubMed Google Scholar

Nikseresht, S., Farshchi Tabrizi, F., Riazi, M., Torabi, F. & Hashemi, S. H. Thermodynamic prediction of interfacial tension of water/oil system with the presence surfactants and salt. Model. Earth Syst. Environ. 8(2), 21932199 (2022).

Article Google Scholar

Mahdaviara, M., Amar, M. N., Ostadhassan, M. & Hemmati-Sarapardeh, A. On the evaluation of the interfacial tension of immiscible binary systems of methane, carbon dioxide, and nitrogen-alkanes using robust data-driven approaches. Alex. Eng. J. 61(12), 1160111614 (2022).

Article Google Scholar

Wang, Y., Shardt, N., Elliott, J. A. & Jin, Z. Highly efficient and accurate gas-alkane binary mixture interfacial tension equations for a broad range of temperatures, pressures, and compositions. SPE J. 27(01), 895913 (2022).

Article Google Scholar

Ng, C. S. W., Djema, H., Amar, M. N. & Ghahfarokhi, A. J. Modeling interfacial tension of the hydrogen-brine system using robust machine learning techniques: Implication for underground hydrogen storage. Int. J. Hydrogen Energy 47(93), 3959539605 (2022).

Article Google Scholar

Rashidi-Khaniabadi, A., Rashidi-Khaniabadi, E., Amiri-Ramsheh, B., Mohammadi, M.-R. & Hemmati-Sarapardeh, A. Modeling interfacial tension of surfactanthydrocarbon systems using robust tree-based machine learning algorithms. Sci. Rep. 13(1), 10836 (2023).

Article ADS PubMed PubMed Central Google Scholar

Gbadamosi, A. et al. New-generation machine learning models as prediction tools for modeling interfacial tension of hydrogen-brine system. Int. J. Hydrogen Energy 50, 4 (2023).

Google Scholar

Mouallem, J., Raza, A., Glatz, G., Mahmoud, M. & Arif, M. Estimation of CO2-brine interfacial tension using machine learning: implications for CO2 geo-storage. J. Mol. Liq. 356, 123672 (2023).

Google Scholar

Jo, J.-M. Effectiveness of normalization pre-processing of big data to the machine learning performance. J. Korea Inst. Electron. Commun. Sci. 14(3), 547552 (2019).

Google Scholar

Carey, C., Boucher, T., Mahadevan, S., Bartholomew, P. & Dyar, M. Machine learning tools formineral recognition and classification from Raman spectroscopy. J. Raman Spectrosc. 46(10), 894903 (2015).

Article ADS Google Scholar

Al Shalabi, L. & Shaaban, Z. (eds.) Normalization as a preprocessing engine for data mining and the approach of preference matrix. In 2006 International Conference on Dependability of Computer Systems (IEEE, 2006).

Talebkeikhah, M., Sadeghtabaghi, Z. & Shabani, M. A comparison of machine learning approaches for prediction of permeability using well log data in the hydrocarbon reservoirs. J. Hum. Earth Future 2(2), 8299 (2021).

Article Google Scholar

Pan, J., Zhuang, Y. & Fong, S. (eds.) The impact of data normalization on stock market prediction: using SVM and technical indicators. In Soft Computing in Data Science: Second International Conference, SCDS 2016, Kuala Lumpur, Malaysia, September 2122, 2016, Proceedings 2 (Springer, 2016).

Peiro Ahmady Langeroudy, K., Kharazi Esfahani, P. & Khorsand Movaghar, M. R. Enhanced intelligent approach for determination of crude oil viscosity at reservoir conditions. Sci. Rep. 13(1), 1666 (2023).

Article ADS PubMed PubMed Central Google Scholar

Dargahi-Zarandi, A., Hemmati-Sarapardeh, A., Shateri, M., Menad, N. A. & Ahmadi, M. Modeling minimum miscibility pressure of pure/impure CO2-crude oil systems using adaptive boosting support vector regression: Application to gas injection processes. J. Petrol. Sci. Eng. 184, 106499 (2020).

Article Google Scholar

Ng, C. S. W., Ghahfarokhi, A. J. & Amar, M. N. Well production forecast in Volve field: Application of rigorous machine learning techniques and metaheuristic algorithm. J. Petrol. Sci. Eng. 208, 109468 (2022).

Article Google Scholar

Talebkeikhah, M. et al. Experimental measurement and compositional modeling of crude oil viscosity at reservoir conditions. J. Taiwan Inst. Chem. Eng. 109, 3550 (2020).

Article Google Scholar

Nait Amar, M. & Zeraibi, N. A combined support vector regression with firefly algorithm for prediction of bottom hole pressure. SN Appl. Sci. 2(1), 23 (2020).

Article Google Scholar

Amar, M. N., Zeraibi, N. & Jahanbani, G. A. Applying hybrid support vector regression and genetic algorithm to water alternating CO2 gas EOR. Greenh. Gases Sci. Technol. 10(3), 613630 (2020).

Article Google Scholar

Sethi, A. Support vector regression tutorial for machine learning. Stat. Comput. 14, 15 (2020).

Google Scholar

Zamani, M. G. et al. A multi-model data fusion methodology for reservoir water quality based on machine learning algorithms and bayesian maximum entropy. J. Clean. Prod. 416, 137885 (2023).

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