Prediction of Interfacial Tension Using Machine Learning: A Review of Applied Techniques in Petrochemical/Reservoir Engineering
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
Citations
IRE Journals:
Okon, John Effiong , Tinuola Udoh , Blessed Emenka
"Prediction of Interfacial Tension Using Machine Learning: A Review of Applied Techniques in Petrochemical/Reservoir Engineering" Iconic Research And Engineering Journals Volume 7 Issue 9 2024 Page 220-235
IEEE:
Okon, John Effiong , Tinuola Udoh , Blessed Emenka
"Prediction of Interfacial Tension Using Machine Learning: A Review of Applied Techniques in Petrochemical/Reservoir Engineering" Iconic Research And Engineering Journals, 7(9)
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