This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Ahmadi, M. A., Ebadi, M., Shokrollahi, A. and Majidi, S. M. J. 2013. Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Applied Soft Computing 13(2): 1085–1098.AhmadiM. A.EbadiM.ShokrollahiA.MajidiS. M. J.2013Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoirApplied Soft Computing1321085109810.1016/j.asoc.2012.10.009Search in Google Scholar
AL-Qutami, T. A. 2017. Heterogeneous Ensemble Learning For Virtual Flow Metering Applications, Master of Science Thesis, Universiti Teknologi PETRONAS.AL-QutamiT. A.2017Heterogeneous Ensemble Learning For Virtual Flow Metering ApplicationsMaster of Science Thesis,Universiti Teknologi PETRONASSearch in Google Scholar
AL-Qutami, T. A., Ibrahim, R., Ismail, I. and Ishak, M. A. 2018. Virtual multiphase flow metering using diverse neural network ensemble and adaptive simulated annealing. Expert Syst. Appl. 93: 72–85.AL-QutamiT. A.IbrahimR.IsmailI.IshakM. A.2018Virtual multiphase flow metering using diverse neural network ensemble and adaptive simulated annealingExpert Syst. Appl.93728510.1016/j.eswa.2017.10.014Search in Google Scholar
American Petroleum Institute (API). 2013. Manual of Petroleum Measurement Standards Chapter 20.3 Measurement of Multiphase Flow.American Petroleum Institute (API)2013Manual of Petroleum Measurement Standards Chapter 20.3 Measurement of Multiphase FlowSearch in Google Scholar
Andrianov, N. 2018. A machine learning approach for virtual flow metering and forecasting. IFAC-Pap. 51(8): 191–196; [Online]. Available: https://arxiv.org/pdf/1802.05698.pdf.AndrianovN.2018A machine learning approach for virtual flow metering and forecastingIFAC-Pap518191196[Online]. Available: https://arxiv.org/pdf/1802.05698.pdf.10.1016/j.ifacol.2018.06.376Search in Google Scholar
Belt, R., Duret, E., Larrey, D., Djoric, B. and Kalali, S. 2011. Comparison of commercial multiphase flow simulators with experimental and field databases. 15th International Conference on Multiphase Production Technology, Cannes, France, June 2011, p. 15. [Online]. Available: https://doi.org/.BeltR.DuretE.LarreyD.DjoricB.KalaliS.2011Comparison of commercial multiphase flow simulators with experimental and field databases15th International Conference on Multiphase Production TechnologyCannes, FranceJune 201115[Online]. Available: https://doi.org/.Search in Google Scholar
Bikmukhametov, T. and Jäschke, J. 2019. Oil production monitoring using gradient boosting machine learning algorithm. IFAC-Pap. 52(1): 514–519.BikmukhametovT.JäschkeJ.2019Oil production monitoring using gradient boosting machine learning algorithmIFAC-Pap.52151451910.1016/j.ifacol.2019.06.114Search in Google Scholar
Bikmukhametov, T. and Jäschke, J. 2020a. Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models. Computers & Chemical Engineering, p. 106834, doi: https://doi.org/10.1016/j.compchemeng.2020.106834.BikmukhametovT.JäschkeJ.2020aCombining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven modelsComputers & Chemical Engineering106834doi: https://doi.org/10.1016/j.compchemeng.2020.10683410.1016/j.compchemeng.2020.106834Search in Google Scholar
Bikmukhametov, T. and Jäschke, J. 2020b. First principles and machine learning virtual flow metering: a literature review. Journal of Petroleum Science and Engineering 184: 106487.BikmukhametovT.JäschkeJ.2020bFirst principles and machine learning virtual flow metering: a literature reviewJournal of Petroleum Science and Engineering18410648710.1016/j.petrol.2019.106487Search in Google Scholar
Canon, J. M., Yau, S., Francisco, A., Angola, B., Espeland, M. and Lundsbakken, K. E. 2015. Online Transient Simulation in Deepwater Operations: Practical Experiences, vol. All Days, doi: 10.4043/25740-MS.CanonJ. M.YauS.FranciscoA.AngolaB.EspelandM.LundsbakkenK. E.2015Online Transient Simulation in Deepwater Operations: Practical Experiencesvol. All Days,10.4043/25740-MSOpen DOISearch in Google Scholar
Fetoui, I. Introduction to IPR and VLP. Production Technology. [Online]. Available: https://production-technology.org/introduction-ipr-vlp/.FetouiIIntroduction to IPR and VLPProduction Technology. [Online]. Available: https://production-technology.org/introduction-ipr-vlp/.Search in Google Scholar
Grimstad, B., Hotvedt, M., Sandnes, A. T., Kolbjørnsen, O. and Imsland, L. S. 2021. Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study. ArXiv Prepr. ArXiv210201391.GrimstadB.HotvedtM.SandnesA. T.KolbjørnsenO.ImslandL. S.2021Bayesian Neural Networks for Virtual Flow Metering: An Empirical StudyArXiv Prepr. ArXiv21020139110.1016/j.asoc.2021.107776Search in Google Scholar
I. O. for S. (ISO). 2003. Measurement of fluid flow by means of pressure differential devices inserted in circular cross-section conduits running full. International Organization for Standardization (ISO), [Online]. Available: https://www.iso.org/standard/28064.html.I. O. for S. (ISO)2003Measurement of fluid flow by means of pressure differential devices inserted in circular cross-section conduits running fullInternational Organization for Standardization (ISO)[Online]. Available: https://www.iso.org/standard/28064.html.Search in Google Scholar
Ishak, M. A., Hasan AL-Qutami, T. A., Ellingsen, H., Ruden, T. and Khaledi, H. 2020. Evaluation of data driven versus multiphase transient flow simulator for virtual flow meter application.IshakM. A.Hasan AL-QutamiT. A.EllingsenH.RudenT.KhalediH.2020Evaluation of data driven versus multiphase transient flow simulator for virtual flow meter application10.4043/30422-MSSearch in Google Scholar
Kim, H., Lee, N. and Seo, Y. 2020. Development and Employment of Dynamic Simulation Integrator for OLGA and HYSYS, vol. All Days.KimH.LeeN.SeoY.2020Development and Employment of Dynamic Simulation Integrator for OLGA and HYSYSvol. All Days.Search in Google Scholar
Mokhtari Jadid, K. 2017. [Online]. Available: https://digitalcommons.lsu.edu/gradschool_dissertations/4303?utm_source=digitalcommons.lsu.edu%2Fgradschool_dissertations%2F4303&utm_medium=PDF&utm_campaign=PDFCoverPages.Mokhtari JadidK.2017[Online]. Available: https://digitalcommons.lsu.edu/gradschool_dissertations/4303?utm_source=digitalcommons.lsu.edu%2Fgradschool_dissertations%2F4303&utm_medium=PDF&utm_campaign=PDFCoverPages.Search in Google Scholar
Naik, K. 2019a. Random Forest(Bootstrap Aggregation) Easily Explained, https://www.youtube.com/watch?v=iajaNLLCOF4.NaikK.2019aRandom Forest(Bootstrap Aggregation) Easily Explainedhttps://www.youtube.com/watch?v=iajaNLLCOF4.Search in Google Scholar
Naik, K. 2019b. Tutorial 42 - Ensemble: What is Bagging (Bootstrap Aggregation)? https://www.youtube.com/watch?v=KIOeZ5cFZ50.NaikK.2019bTutorial 42 - Ensemble: What is Bagging (Bootstrap Aggregation)?https://www.youtube.com/watch?v=KIOeZ5cFZ50.Search in Google Scholar
Naik, K. 2020. Machine Learning-Bias And Variance In Depth Intuition| Overfitting Underfitting, https://www.youtube.com/watch?v=BqzgUnrNhFM.NaikK.2020Machine Learning-Bias And Variance In Depth Intuition| Overfitting Underfittinghttps://www.youtube.com/watch?v=BqzgUnrNhFM.Search in Google Scholar
Norwegian Society for Oil and Gas Measurement (NFOGM). 2005. Handbook of Multiphase Flow Metering. The Norwegian Society for Oil and Gas Measurement (NFOGM).Norwegian Society for Oil and Gas Measurement (NFOGM)2005Handbook of Multiphase Flow MeteringThe Norwegian Society for Oil and Gas Measurement (NFOGM)Search in Google Scholar
Opitz, D. and Maclin, R. 1999. Popular ensemble methods: an empirical study. Journal of Artificial Intelligence Research 11: 169–198.OpitzD.MaclinR.1999Popular ensemble methods: an empirical studyJournal of Artificial Intelligence Research1116919810.1613/jair.614Search in Google Scholar
PETRONAS. 2013. PETRONAS Technical Standards Liquid Hydrocarbon Custody Transfer and Allocation Measurement.PETRONAS2013PETRONAS Technical Standards Liquid Hydrocarbon Custody Transfer and Allocation MeasurementSearch in Google Scholar
PETRONAS. 2015. PETRONAS Technical Standards Multiphase Flow Metering System. PETROLIAM NASIONAL BERHAD.PETRONAS2015PETRONAS Technical Standards Multiphase Flow Metering SystemPETROLIAM NASIONAL BERHADSearch in Google Scholar
Poulisse, H., van Overschee, P., Briers, J., Moncur, C. and Goh, K.-C. 2006. Continuous Well Production Flow Monitoring and Surveillance, presented at the Intelligent Energy Conference and Exhibition, doi: 10.2118/99963-MS.PoulisseH.van OverscheeP.BriersJ.MoncurC.GohK.-C.2006Continuous Well Production Flow Monitoring and Surveillancepresented at the Intelligent Energy Conference and Exhibition10.2118/99963-MSOpen DOISearch in Google Scholar
Powerhouse, K. 2020. What is the difference between bagging and boosting methods in ensemble learning? https://www.youtube.com/watch?v=UeYG64Hm7Es&t=82s.PowerhouseK.2020What is the difference between bagging and boosting methods in ensemble learning?https://www.youtube.com/watch?v=UeYG64Hm7Es&t=82s.Search in Google Scholar
Process Industry Practices (PIP). 2015. Flow Measurement Guidelines. Process Industry Practices (PIP), [Online]. Available: https://standards.globalspec.com/std/10158393/pip-pcefl001.Process Industry Practices (PIP)2015Flow Measurement GuidelinesProcess Industry Practices (PIP)[Online]. Available: https://standards.globalspec.com/std/10158393/pip-pcefl001.Search in Google Scholar
Semicolon, T. 2017. Scikit Learn Ensemble Learning, Bootstrap Aggregating (Bagging) and Boosting, https://www.youtube.com/watch?v=X3Wbfb4M33w.SemicolonT.2017Scikit Learn Ensemble Learning, Bootstrap Aggregating (Bagging) and Boostinghttps://www.youtube.com/watch?v=X3Wbfb4M33w.Search in Google Scholar
Turbulent Flux, A. S. 2020. Turbulent Flux. [Online]. Available https://turbulentflux.com.Turbulent Flux A. S.2020Turbulent Flux[Online]. Available https://turbulentflux.com.Search in Google Scholar
Wolpert, D. H. 1992. Stacked generalization. Neural Netw. 5(2): 241–259.WolpertD. H.1992Stacked generalizationNeural Netw.5224125910.1016/S0893-6080(05)80023-1Search in Google Scholar