Prediction of Interfacial Tension Using Machine Learning: A Review of Applied Techniques in Petrochemical/Reservoir Engineering

References

[1] Bui, T. et al. Water/oil interfacial tension reduction—An interfacial entropy driven process. JPCCP 23(44), 25075–25085 (2021).

[2] 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).

[3] 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).

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

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

[6] Pereira, L. M., Chapoy, A., Burgass, R. & Tohidi, B. Interfacial tension of CO2+ brine systems. Exp. Predict. Model. 103, 64–75 (2017).

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

[8] 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), 1332–1363 (2022).

[9] Singh, S. K., Chaurasia, A. & Verma, A. Basics of Density Functional Teory, Molecular Dynamics, and Monte Carlo Simulation Techniques in Materials Science. In Coating Materials: Computational Aspects, Applications and Challenges (eds Verma, A. et al.) 111–124 (Springer, 2023).

[10] Zhao, X., Duan, W., Zeng, X. & Liu, Y. J. Measurements of surface tension of R1234yf and R1234ze (E). Int. J. Refrig. 63(1), 21–26 (2018). 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).Plimpton, S. Fast Parallel Algorithms for Short-Range Molecular Dynamics. J. Comput. Phys. 1995, 117, 1−19.

[11] Brown, W. M.; Wang, P.; Plimpton, S. J.; Tharrington, A. N. Implementing Molecular Dynamics on Hybrid High Performance Computers−Short Range Forces. Comput. Phys. Commun. 2011, 182, 898−911.

[12] Brown, W. M.; Kohlmeyer, A.; Plimpton, S. J.; Tharrington, A. N. Implementing Molecular Dynamics on Hybrid High Performance Computers−Particle−Particle Particle-Mesh. Comput. Phys. Commun. 2012, 183, 449−459.

[13] Brown, W. M.; Yamada, M. Implementing Molecular Dynamics on Hybrid High Performance Computers-Three-Body Potentials. Comput. Phys. Commun. 2013, 184, 2785−2793.

[14] Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg. Scikit-Learn: Machine Learning in Python. Journal of Machine Learning Research 2011, 12, 2825−2830.toward predicting interfacial tension of impure and pure co2-brine systems using robust correlative approaches chen wei-yu,lin sun, jiyong zhou, xuguang li*, liping huang, guang xiamengand kui wang

[15] Gary R. Jerauld, Aboulghasem Kazemi,An improved simple correlation for accurate estimation of CO2-Brine interfacial tension at reservoir conditions,Journal of Petroleum Science and Engineering,Volume 208, Part C,2022,109537,ISSN 0920-4105,https://doi.org/10.1016/j.petrol.2021.109537.

[16] Mutallep, Mhriay, Jiang, Lanlan, Fu, Jin, Wang, Zhe, Yu, Tianbo, Lu, Zilong, Liu, Yu; 2018/06/01; Effects of Na+, K+, Ca2+, and Mg2+ cations on CO2–brine interfacial tension under offshore storage conditions, Vol 8; DO -10.1002/ghg.1787; JO - Greenhouse Gases: Science and TechnologyER -

[17] Cheng Zhang, Milei Wang,-CO2/brine interfacial tension for geological CO2 storage: A systematic review,Journal of Petroleum Science and Engineering,Volume 220, Part A,2023,111154,ISSN 0920-4105,https://doi.org/10.1016/j.petrol.2022.111154.(https://www.sciencedirect.com/science/article/pii/S0920410522010063)

[18] Wanli Xing, Yongchen Song, Yi Zhang, Masahiro Nishio, Yangchun Zhan, Weiwei Jian, Yong Shen, Research Progress of the Interfacial Tension in Supercritical CO2-water/oil System,Energy Procedia,Volume 37, 2013,Pages 6928-6935,ISSN 1876-6102,https://doi.org/10.1016/j.egypro.2013.06.625.(https://www.sciencedirect.com/science/article/pii/S1876610213008680)

[19] Osama Massarweh, Ahmad S. Abushaikha, The use of surfactants in enhanced oil recovery: A review of recent advances, Energy Reports, Volume 6,2020, Pages 3150-3178, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2020.11.009. (https://www.sciencedirect.com/science/article/pii/S2352484720314347)

[20] Friberg, Stig & Corkery, Robert & Blute, Irena. (2011). Phase Inversion Temperature (PIT) Emulsification Process. Journal of Chemical & Engineering Data. 56. 4282–4290. 10.1021/je101179s.

[21] Zhang, Madhar Sahib Azad, J.J. Trivedi, IFT or wettability alteration: What is more important for oil recovery in oil-wet formation?, Fuel, Volume 291, 2021,119986,ISSN 0016-2361, https://doi.org/10.1016/j.fuel.2020.119986. (https://www.sciencedirect.com/science/article/pii/S0016236120329823)

[22] Hamed Farhadi, Shahab Ayatollahi, Mobeen Fatemi, The effect of brine salinity and oil components on dynamic IFT behavior of oil-brine during low salinity water flooding: Diffusion coefficient, EDL establishment time, and IFT reduction rate, Journal of Petroleum Science and Engineering, Volume 196,2021, 107862, ISSN 0920-4105, https://doi.org/10.1016/j.petrol.2020.107862.(https://www.sciencedirect.com/science/article/pii/S0920410520309220)

[23] Mortada Daaou, Dalila Bendedouch,Water pH and surfactant addition effects on the stability of an Algerian crude oil emulsion,Journal of Saudi Chemical Society, Volume 16, Issue 3, 2012, Pages 333-337,ISSN 1319-6103, https://doi.org/10.1016/j.jscs.2011.05.015.(https://www.sciencedirect.com/science/article/pii/S1319610311001141)

[24] Sharma, Mukul & Filoco, P.. (2000). Effect of Brine Salinity and Crude-Oil Properties on Oil Recovery and Residual Saturations. SPE Journal - SPE J. 5. 293-300. 10.2118/65402-PA.

[25] Menad Nait Amar, Mohammadhadi Shateri, Abdolhossein Hemmati-Sarapardeh, Alireza Alamatsaz, Modeling oil-brine interfacial tension at high pressure and high salinity conditions, Journal of Petroleum Science and Engineering, Volume 183, 2019, 106413, ISSN 0920-4105, https://doi.org/10.1016/j.petrol.2019.106413. (https://www.sciencedirect.com/science/article/pii/S0920410519308344)

[26] 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).

[27] Ghahfarokhi, A. J. & Amar, M. N. Well production forecast in Volve feld: Application of rigorous machine learning techniques and metaheuristic algorithm. J. Petrol. Sci. Eng. 208, 109468 (2022).

[28] Talebkeikhah, M. et al. Experimental measurement and compositional modeling of crude oil viscosity at reservoir conditions. J. Taiwan Inst. Chem. Eng. 109, 35–50 (2020).

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

[30] 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), 613–630 (2020). Sethi, A. Support vector regression tutorial for machine learning. Stat. Comput. 14, 1–5 (2020).

[31] 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).

[32] Danesh, A., Ehsani, M., Moghadas Nejad, F. & Zakeri, H. Prediction model of crash severity in imbalanced dataset using data leveling methods and metaheuristic optimization algorithms. Int. J. Crashworthin. 27(6), 1869–1882 (2022).

[33] Wanan Liu, Hong Fan, Meng Xia, Credit scoring based on tree-enhanced gradient boosting decision trees, Expert Systems with Applications, Volume 189, 2022, 116034, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2021.116034. (https://www.sciencedirect.com/science/article/pii/S0957417421013798)

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

[35] Wu, J., Li, Y. & Ma, Y. (eds.) Comparison of XGBoost and the neural network model on the class-balanced datasets. In 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer (ICFTIC) (IEEE, 2021). Chen, T. & Guestrin, C. (eds.) Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016).

[36] Larestani, A., Mousavi, S. P., Hadavimoghaddam, F. & Hemmati-Sarapardeh, A. Predicting formation damage of oil felds due to mineral scaling during water-fooding operations: Gradient boosting decision tree and cascade-forward back-propagation network. J. Petrol. Sci. Eng. 208, 109315 (2022).

[37] 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).

[38] Dorogush, A.V., Ershov, V. & Gulin, A. CatBoost: Gradient boosting with categorical features support. arXiv Preprint arXiv:181011363 (2018).

[39] Huang, G. et al. Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions. J. Hydrol. 574, 1029–1041 (2019).

[40] X. He, J. Pan, O. Jin, T. Xu, B. Liu, T. Xu, Y. Shi, A. Atallah, R. Herbrich, S. Bowers, et al. Practical lessons from predicting clicks on ads at facebook. In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising, pages 1–9. ACM, 2014

[41] Debjani Chakraborty, Ahona Ghosh, Sriparna Saha, Chapter 2 - A survey on Internet-of-Thing applications using electroencephalogram, Editor(s): Valentina Emilia Balas, Vijender Kumar Solanki, Raghvendra Kumar,Emergence of Pharmaceutical Industry Growth with Industrial IoT Approach,Academic Press,2020,Pages 21-47,ISBN 9780128195932,https://doi.org/10.1016/B978-0-12-819593-2.00002-9. (https://www.sciencedirect.com/science/article/pii/B9780128195932000029)

[42] Neha Sharma, Usha Batra, Sherin Zafar, Chapter 1 - A neoteric swarm intelligence stationed IOT–IWD algorithm for revolutionizing pharmaceutical industry leading to digital health,https://doi.org/10.1016/B978-0-12-819593-2.00001-7. (https://www.sciencedirect.com/science/article/pii/B9780128195932000017)

[43] Subhashree Sahoo, Amiya Bhusan Bagjadab, Sushree Bibhuprada B. Priyadarshini, Chapter 10 - Smart pills: a complete revolutionary technology than endoscopy,Emergence of Pharmaceutical Industry Growth with Industrial IoT Approach,Academic Press,2020,Pages 275-303,ISBN 9780128195932, https://doi.org/10.1016/B978-0-12-819593-2.00010-8. (https://www.sciencedirect.com/science/article/pii/B9780128195932000108)

[44] Vikram Puri, Raghvendra Kumar, Dac Nhuong Le, Sandeep Singh Jagdev, Nidhi Sachdeva,https://doi.org/10.1016/B978-0-12-819593-2.00011-X. (https://www.sciencedirect.com/science/article/pii/B978012819593200011X)

[45] Friedman, Jerome; Trevor Hastie; Rob Tibshirani (2010). "Regularization Paths for Generalized Linear Models via Coordinate Descent". Journal of Statistical Software. 33 (1): 1–22. doi:10.18637/jss.v033.i01. PMC 2929880. PMID 2080872

[46] Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction; Springer Science & Business Media, 2009.

[47] Okon, John and Udoh, Tinuola and Emenka, Blessed, Prediction of Crude Oil-Brine Interfacial Tension Based on Surfactant Characteristics Using Artificial Intelligence. Available at SSRN: https://ssrn.com/abstract=4770150 or http://dx.doi.org/10.2139/ssrn.4770150

@article{1705605,
      author = {Okon, John Effiong, Tinuola Udoh, Blessed Emenka},
      title = {Prediction of Interfacial Tension Using Machine Learning: A Review of Applied Techniques in Petrochemical/Reservoir Engineering},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {9},
      pages = {220-235},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705605.pdf},
      abstract = {As efforts to increase oil reserves through enhanced oil recovery projects increase globally, interfacial tension in crude oil - brine systems is becoming increasingly significant. In porous media, displacement processes and multi-phase flow are directly impacted by interfacial tension. It has an impact on oil field emulsions' behavior as well. The majority of documented two-phase flow and displacement procedures executed under varying interfacial tensions have been executed for either water-gas, oil-water or oil-gas two-phase systems. One significant factor influencing the displacements of water/oil and water/oil/gas is the interfacial tension between crude oil and brine. Adhesion tension, capillary pressure, capillary number, and the dimensionless time for imbibition are all influenced by interfacial tension. A crucial physical characteristic that influences several processes in the oil and gas sector, including enhanced oil recovery, multi-phase flow, and emulsion stability, is interfacial tension (IFT). Increasing the efficiency and optimizing these processes depend on accurate IFT prediction. This article reviews the various techniques that have been applied to make interfacial tension  (IFT) predictions in liquid-liquid and liquid-vapour systems, exploring advanced  machine learning (ML) techniques  in terms of the variables used for modelling, variable relevance,  internal parameters tuning, performance analysis and future prospects of the most advanced algorithms. The Gradient Boosting (GB), Elastic Net Regression (EN), AdaBoost, SVR, CatBoost, and XGBoost algorithms are explored and results of their application from different studies compared.},
      keywords = {Interfacial Tension, Machine Learning, Algorithm, Boosting},
      month = {March},
  }