[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128247-en":3,"doc-seo-128247-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128247,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Machine learning-based estimation of crude oil-nitrogen interfacial tension","Accurate estimation of nitrogen–crude oil interfacial tension (IFT) is critical for optimizing nitrogen-based gas injection during enhanced oil recovery. The study develops data-driven intelligent models using eight machine learning methods—Decision Tree, AdaBoost, Random Forest, KNN, Ensemble Learning, SVM, CNN, and MLP-ANN—trained on experimental measurements from real crude oil samples rather than synthetic n-alkanes. Pressure, temperature, and crude oil API negatively affect IFT, with pressure most influential. Random Forest achieves the best predictive performance with strong R-squared and low errors, and reliable trend capture across input parameters.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nMachine learning-based estimation of crude oil-nitrogen interfacial tension  \nSafia Obaidur Rab1,2, Subhash Chandra3, Abhinav Kumar4,14,15, Pinank Patel5, Mohammed Al-Farouni6,7,8, Soumya V. Menon9, Bandar R. Alsehli10, Mamata Chahar11, Manmeet Singh12 & Mahmood Kiani13􀀍  \nAccurate 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.  \nKeywords Crude oil – Nitrogen IFT, Machine learning, Sensitivity analysis, Outlier detection  \nThe inefficacy of oil retrieval during primary and secondary production stages has engendered an accelerated maturation of numerous methods for the reduction of residual oil saturation antecedent to the permanent discontinuation of oil reservoir operation1,2. For the purpose of augmenting oil recovery, the introduction of gas into oil reservoirs has proven to be a widely adopted practice, facilitating an enhancement of oil retrieval through the injection of an assortment of gaseous media, namely, natural gas, enriched natural gas, carbon dioxide, nitrogen, or flue gas3,4. Within the spectrum of available gas types, carbon dioxide has garnered recognition  \n1Central Labs, King Khalid University, P.O. Box 960, AlQura’a, Abha, Saudi Arabia. 2Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia. 3Department of Electrical Engineering, GLA University, Mathura 281406, India. 4Department of Nuclear and Renewable Energy, Ural Federal University Named after the First President of Russia Boris Yeltsin, Ekaterinburg 620002, Russia. 5Department of Mechanical Engineering, Faculty of Engineering & Technology, Marwadi University Research Center, Marwadi University, Rajkot 360003, Gujarat, India. 6Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq. 7Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq. 8Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University of Babylon, Babylon, Iraq.  \n9Department of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India. 10Department of Chemistry, Faculty of Science, Taiba","cbCaiqPwnu89X1r5","https://ap.wps.com/l/cbCaiqPwnu89X1r5","pdf",5960733,2,1,18,"English","en",105,"# Abstract\n## Machine learning models for IFT prediction\n## Experimental data and model evaluation\n## Sensitivity analysis of influencing factors\n## Best-performing model and applications","[{\"question\":\"Why is nitrogen–crude oil interfacial tension estimation important in enhanced oil recovery?\",\"answer\":\"Because accurate IFT values are necessary to optimize nitrogen-based gas injection and improve oil recovery performance in underground reservoirs.\"},{\"question\":\"Which machine learning methods are used to predict crude oil–nitrogen IFT?\",\"answer\":\"Decision Tree, AdaBoost, Random Forest, K-nearest Neighbors, Ensemble Learning, Support Vector Machine, Convolutional Neural Network, and Multilayer Perceptron Artificial Neural Network.\"},{\"question\":\"What factors influence IFT, and which one is most effective?\",\"answer\":\"Pressure, temperature, and crude oil API all negatively affect IFT, and pressure is the most effective factor.\"}]","Machine learning-based estimation of crude oil-nitrogen interfacial tension | 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is nitrogen–crude oil interfacial tension estimation important in enhanced oil recovery?","Question",{"text":76,"@type":77},"Because accurate IFT values are necessary to optimize nitrogen-based gas injection and improve oil recovery performance in underground reservoirs.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning methods are used to predict crude oil–nitrogen IFT?",{"text":81,"@type":77},"Decision Tree, AdaBoost, Random Forest, K-nearest Neighbors, Ensemble Learning, Support Vector Machine, Convolutional Neural Network, and Multilayer Perceptron Artificial Neural Network.",{"name":83,"@type":74,"acceptedAnswer":84},"What factors influence IFT, and which one is most effective?",{"text":85,"@type":77},"Pressure, temperature, and crude oil API all negatively affect IFT, and pressure is the most effective 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