Machine learning model to predicting synergy of ultrasonication and solvation impacts on crude oil viscosity
Khan N. Razavifar M. Ahmad Q.A. Siyar M. Riazi M. Khan W. Qajar J.
December 2025Nature Research
Scientific Reports
2025#15Issue 1
The growing energy demand is attracting a lot of interest in heavy crude oil. While various conventional methods have been employed to reduce the viscosity of crude oil, the challenges associated with these techniques have driven researchers to seek economical and environmentally sustainable alternatives. In this context, combining traditional methods, such as solvent treatment, with ultrasonic radiation presents a promising, yet complex solution. In this study, we develop a machine learning-based algorithm to rigorously predict the synergistic effects of ultrasonication and solvation on crude oil viscosity. This research is divided into two parts. First, we conducted experimental measurements of crude oil viscosity following treatment with ultrasound and/or n-heptane solvent. It was found that the optimum irradiation period for samples containing 0% to 16% n-heptane was 8 min. Notably, after 8 min of treatment, the viscosity decreased by over 34% for samples with 16% n-heptane and by more than 47% for those with 0% n-heptane. When the n-heptane concentration was increased to 22% and 30%, the required sonication time increased by 2 to 10 min. In the former case, viscosity was reduced by more than 50%, while in the latter, it decreased by over 48%. In the second part of the study, a Machine Learning (ML) model was developed using the experimental data. In particular, a Random Forest Regressor (RFR) model was applied, with results demonstrating high reliability based on RMSE and R2 values for training (3.3395, 0.9764), validation (3.0166, 0.9602), and testing (2.4778, 0.9557). Considering the significance of features, n-Heptane (0.54) and irradiation time (0.45) were the key predictors, with n-heptane showing slightly greater impact on error reduction. The application of this model to additional datasets from other oil fields shows significant promise for future research and practical implementation.
Crude oil , Machine learning , Random forest regressor , Ultrasonic waves , Viscosity reduction
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Department of Mechanical Engineering (Well Engineering), International College of Engineering and Management, P.O. Box 2511, C.P.O Seeb, Muscat, P.C. 111, Oman
Faculty of Chemical and Petroleum Engineering, University of Tabriz, Tabriz, 5166616471, Iran
School of Mines, China University of Mining & Technology, Xuzhou, 221116, China
School of Chemical and Materials Engineering, SCME, National University of Sciences and Technology, NUST, H-12, Islamabad, 44000, Pakistan
School of Mining and Geoscience, Nazarbayev University, Kabanbay Batyr 53, Astana, 010000, Kazakhstan
Fuzhou University of International Studies and Trade, Fazhou, 350200, China
Department of Petroleum Engineering, School of Chemical and Petroleum Engineering, Shiraz University, Shiraz, 7134851154, Iran
Department of Earth Sciences, Faculty of Geosciences, Utrecht University, Utrecht, 3584 CB, Netherlands
Department of Mechanical Engineering (Well Engineering)
Faculty of Chemical and Petroleum Engineering
School of Mines
School of Chemical and Materials Engineering
School of Mining and Geoscience
Fuzhou University of International Studies and Trade
Department of Petroleum Engineering
Department of Earth Sciences
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