Machine learning-based estimation of crude oil-nitrogen interfacial tension
Abstract Accurate estimation of interfacial tension (IFT) between nitrogen and crude oil during nitrogen-based gas injection into oil reservoirs is imperative. The previous research works dealing with prediction of IFT of oil and nitrogen systems consider synthetic oil samples such n-alkanes. In thi...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-85106-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841544666331217920 |
|---|---|
| author | Safia Obaidur Rab Subhash Chandra Abhinav Kumar Pinank Patel Mohammed Al-Farouni Soumya V. Menon Bandar R. Alsehli Mamata Chahar Manmeet Singh Mahmood Kiani |
| author_facet | Safia Obaidur Rab Subhash Chandra Abhinav Kumar Pinank Patel Mohammed Al-Farouni Soumya V. Menon Bandar R. Alsehli Mamata Chahar Manmeet Singh Mahmood Kiani |
| author_sort | Safia Obaidur Rab |
| collection | DOAJ |
| description | Abstract Accurate estimation of interfacial tension (IFT) between nitrogen and crude oil during nitrogen-based gas injection into oil reservoirs is imperative. The previous research works dealing with prediction of IFT of oil and nitrogen systems consider synthetic oil samples such n-alkanes. In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil – nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs. Several statistical indices and graphical approaches are used as accuracy performance indicators. The results show that virtually all the gathered datapoints are suitable for the purpose of model development. The sensitivity analysis indicated that pressure, temperature and crude oil API all negatively affect the IFT, with pressure being the most effective factor. The evaluation study proved that Random Forest is the most accurate developed intelligent model as it was characterized with acceptable R-squared (0.959), mean square error (1.65), average absolute relative error (6.85%) of unseen test datapoints as well as with correct trend prediction of IFT with regard to all input parameters of pressure, temperature and crude oil API. The developed model can be considered an accurate an easy-to-use tool for the prediction of crude oil/N2 IFT values for enhance oil recovery study optimization and upstream reservoir investigations. |
| format | Article |
| id | doaj-art-5be603f7a1874d588e48496a16249078 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5be603f7a1874d588e48496a162490782025-01-12T12:22:02ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-025-85106-yMachine learning-based estimation of crude oil-nitrogen interfacial tensionSafia Obaidur Rab0Subhash Chandra1Abhinav Kumar2Pinank Patel3Mohammed Al-Farouni4Soumya V. Menon5Bandar R. Alsehli6Mamata Chahar7Manmeet Singh8Mahmood Kiani9Central Labs, King Khalid UniversityDepartment of Electrical Engineering, GLA UniversityDepartment of Nuclear and Renewable Energy, Ural Federal University Named after the First President of Russia Boris YeltsinDepartment of Mechanical Engineering, Faculty of Engineering & Technology, Marwadi University Research Center, Marwadi UniversityDepartment of Computers Techniques Engineering, College of Technical Engineering, The Islamic UniversityDepartment of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University)Department of Chemistry, Faculty of Science, Taibah UniversityDepartment of Chemistry, NIMS Institute of Engineering & Technology, NIMS University RajasthanDepartment of Applied Sciences, Chandigarh Engineering College, Chandigarh Group of Colleges-JhanjeriYoung Researchers and Elite Club, Omidiyeh Branch, Islamic Azad UniversityAbstract Accurate estimation of interfacial tension (IFT) between nitrogen and crude oil during nitrogen-based gas injection into oil reservoirs is imperative. The previous research works dealing with prediction of IFT of oil and nitrogen systems consider synthetic oil samples such n-alkanes. In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil – nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs. Several statistical indices and graphical approaches are used as accuracy performance indicators. The results show that virtually all the gathered datapoints are suitable for the purpose of model development. The sensitivity analysis indicated that pressure, temperature and crude oil API all negatively affect the IFT, with pressure being the most effective factor. The evaluation study proved that Random Forest is the most accurate developed intelligent model as it was characterized with acceptable R-squared (0.959), mean square error (1.65), average absolute relative error (6.85%) of unseen test datapoints as well as with correct trend prediction of IFT with regard to all input parameters of pressure, temperature and crude oil API. The developed model can be considered an accurate an easy-to-use tool for the prediction of crude oil/N2 IFT values for enhance oil recovery study optimization and upstream reservoir investigations.https://doi.org/10.1038/s41598-025-85106-yCrude oil – Nitrogen IFTMachine learningSensitivity analysisOutlier detection |
| spellingShingle | Safia Obaidur Rab Subhash Chandra Abhinav Kumar Pinank Patel Mohammed Al-Farouni Soumya V. Menon Bandar R. Alsehli Mamata Chahar Manmeet Singh Mahmood Kiani Machine learning-based estimation of crude oil-nitrogen interfacial tension Scientific Reports Crude oil – Nitrogen IFT Machine learning Sensitivity analysis Outlier detection |
| title | Machine learning-based estimation of crude oil-nitrogen interfacial tension |
| title_full | Machine learning-based estimation of crude oil-nitrogen interfacial tension |
| title_fullStr | Machine learning-based estimation of crude oil-nitrogen interfacial tension |
| title_full_unstemmed | Machine learning-based estimation of crude oil-nitrogen interfacial tension |
| title_short | Machine learning-based estimation of crude oil-nitrogen interfacial tension |
| title_sort | machine learning based estimation of crude oil nitrogen interfacial tension |
| topic | Crude oil – Nitrogen IFT Machine learning Sensitivity analysis Outlier detection |
| url | https://doi.org/10.1038/s41598-025-85106-y |
| work_keys_str_mv | AT safiaobaidurrab machinelearningbasedestimationofcrudeoilnitrogeninterfacialtension AT subhashchandra machinelearningbasedestimationofcrudeoilnitrogeninterfacialtension AT abhinavkumar machinelearningbasedestimationofcrudeoilnitrogeninterfacialtension AT pinankpatel machinelearningbasedestimationofcrudeoilnitrogeninterfacialtension AT mohammedalfarouni machinelearningbasedestimationofcrudeoilnitrogeninterfacialtension AT soumyavmenon machinelearningbasedestimationofcrudeoilnitrogeninterfacialtension AT bandarralsehli machinelearningbasedestimationofcrudeoilnitrogeninterfacialtension AT mamatachahar machinelearningbasedestimationofcrudeoilnitrogeninterfacialtension AT manmeetsingh machinelearningbasedestimationofcrudeoilnitrogeninterfacialtension AT mahmoodkiani machinelearningbasedestimationofcrudeoilnitrogeninterfacialtension |