Machine learning model to predicting synergy of ultrasonication and solvation impacts on crude oil viscosity
Abstract 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 sustainabl...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-14752-z |
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| author | Nasir Khan Mehdi Razavifar Qazi Adnan Ahmad Muhammad Siyar Masoud Riazi Waqar Khan Jafar Qajar |
| author_facet | Nasir Khan Mehdi Razavifar Qazi Adnan Ahmad Muhammad Siyar Masoud Riazi Waqar Khan Jafar Qajar |
| author_sort | Nasir Khan |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-36fa5a35601b43a8985fe9fac1c3b72a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-36fa5a35601b43a8985fe9fac1c3b72a2025-08-20T03:45:57ZengNature PortfolioScientific Reports2045-23222025-08-0115111110.1038/s41598-025-14752-zMachine learning model to predicting synergy of ultrasonication and solvation impacts on crude oil viscosityNasir Khan0Mehdi Razavifar1Qazi Adnan Ahmad2Muhammad Siyar3Masoud Riazi4Waqar Khan5Jafar Qajar6Department of Mechanical Engineering (Well Engineering), International College of Engineering and ManagementFaculty of Chemical and Petroleum Engineering, University of TabrizSchool of Mines, China University of Mining & TechnologySchool of Chemical and Materials Engineering, SCME, National University of Sciences and Technology, NUSTSchool of Mining and Geoscience, Nazarbayev UniversityFuzhou University of International Studies and TradeDepartment of Petroleum Engineering, School of Chemical and Petroleum Engineering, Shiraz UniversityAbstract 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.https://doi.org/10.1038/s41598-025-14752-zViscosity reductionCrude oilUltrasonic wavesRandom forest regressorMachine learning |
| spellingShingle | Nasir Khan Mehdi Razavifar Qazi Adnan Ahmad Muhammad Siyar Masoud Riazi Waqar Khan Jafar Qajar Machine learning model to predicting synergy of ultrasonication and solvation impacts on crude oil viscosity Scientific Reports Viscosity reduction Crude oil Ultrasonic waves Random forest regressor Machine learning |
| title | Machine learning model to predicting synergy of ultrasonication and solvation impacts on crude oil viscosity |
| title_full | Machine learning model to predicting synergy of ultrasonication and solvation impacts on crude oil viscosity |
| title_fullStr | Machine learning model to predicting synergy of ultrasonication and solvation impacts on crude oil viscosity |
| title_full_unstemmed | Machine learning model to predicting synergy of ultrasonication and solvation impacts on crude oil viscosity |
| title_short | Machine learning model to predicting synergy of ultrasonication and solvation impacts on crude oil viscosity |
| title_sort | machine learning model to predicting synergy of ultrasonication and solvation impacts on crude oil viscosity |
| topic | Viscosity reduction Crude oil Ultrasonic waves Random forest regressor Machine learning |
| url | https://doi.org/10.1038/s41598-025-14752-z |
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