Bui, T. et al. Water\/oil interfacial tension reductionAn interfacial entropy driven process. JPCCP 23(44), 2507525085 (2021). <\/p>\n
ADS Google Scholar <\/p>\n
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). <\/p>\n
Book Google Scholar <\/p>\n
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). <\/p>\n
Article ADS PubMed PubMed Central Google Scholar <\/p>\n
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). <\/p>\n
Article ADS PubMed PubMed Central Google Scholar <\/p>\n
Kalatehno, J. M. & Khamehchi, E. A novel packer fluid for completing HP\/HT oil and gas wells. J. Petrol. Sci. Eng. 203, 108538 (2021). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Article ADS PubMed PubMed Central Google Scholar <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
Pereira, L. M., Chapoy, A., Burgass, R. & Tohidi, B. Interfacial tension of CO2+ brine systems. Exp. Predict. Model. 103, 6475 (2017). <\/p>\n
Google Scholar <\/p>\n
Kim, B. et al. Ensemble machine learning-based approach for predicting of FRP-concrete interfacial bonding. Mathematics 10(2), 231 (2022). <\/p>\n
Article Google Scholar <\/p>\n
Tadros, T. Gibbs adsorption isotherm. In Encyclopedia of Colloid and Interface Science (Tadros, T. ed.). 626 (Springer, 2013). <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Chapter Google Scholar <\/p>\n
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). <\/p>\n
Google Scholar <\/p>\n
Clegg, C. Contact Angle Made Easy: Carl Clegg (2013). <\/p>\n
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). <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Google Scholar <\/p>\n
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). <\/p>\n
Article ADS PubMed PubMed Central Google Scholar <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
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. <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
Dehaghani, A. H. S. & Soleimani, R. Estimation of interfacial tension for geological CO2 storage. Chem. Eng. Technol. 42(3), 680689 (2019). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Article PubMed Google Scholar <\/p>\n
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). <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
Cui, Z. & Li, H. Toward accurate density and interfacial tension modeling for carbon dioxide\/water mixtures. Petrol. Sci. 18, 509529 (2021). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
Bui, T. et al. Water\/oil interfacial tension reductionAn interfacial entropy driven process. Phys. Chem. Chem. Phys. 23(44), 2507525085 (2021). <\/p>\n
Article PubMed Google Scholar <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Article ADS PubMed Google Scholar <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Article ADS PubMed PubMed Central Google Scholar <\/p>\n
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). <\/p>\n
Google Scholar <\/p>\n
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). <\/p>\n
Google Scholar <\/p>\n
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). <\/p>\n
Google Scholar <\/p>\n
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). <\/p>\n
Article ADS Google Scholar <\/p>\n
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). <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
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). <\/p>\n
Article ADS PubMed PubMed Central Google Scholar <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
Talebkeikhah, M. et al. Experimental measurement and compositional modeling of crude oil viscosity at reservoir conditions. J. Taiwan Inst. Chem. Eng. 109, 3550 (2020). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
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). <\/p>\n
Article Google Scholar <\/p>\n
Sethi, A. Support vector regression tutorial for machine learning. Stat. Comput. 14, 15 (2020). <\/p>\n
Google Scholar <\/p>\n
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). <\/p>\n
<\/p>\n
The rest is here: Bui, T. et al. Water\/oil interfacial tension reductionAn interfacial entropy driven process. Continue reading
\nMachine learning approaches for estimating interfacial tension between oil\/gas and oil\/water systems: a performance ... - Nature.com<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"