{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T04:47:18Z","timestamp":1784004438187,"version":"3.55.0"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T00:00:00Z","timestamp":1715126400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T00:00:00Z","timestamp":1715126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012632","name":"Nazarbayev University","doi-asserted-by":"publisher","award":["091019CRP2103"],"award-info":[{"award-number":["091019CRP2103"]}],"id":[{"id":"10.13039\/501100012632","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s00521-024-09821-9","type":"journal-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T04:01:38Z","timestamp":1715140898000},"page":"14503-14526","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Novel robust Elman neural network-based predictive models for bubble point oil formation volume factor and solution gas\u2013oil ratio using experimental data"],"prefix":"10.1007","volume":"36","author":[{"given":"Kamiab","family":"Kahzadvand","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maryam","family":"Mahmoudi Kouhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehdi","family":"Ghasemi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7740-9109","authenticated-orcid":false,"given":"Ali","family":"Shafiei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,8]]},"reference":[{"key":"9821_CR1","unstructured":"Katz DL (1942) Prediction of the shrinkage of crude oils. In: Drilling and production practice. OnePetro"},{"key":"9821_CR2","doi-asserted-by":"publisher","first-page":"108567","DOI":"10.1016\/j.est.2023.108567","volume":"72","author":"K Kohzadvand","year":"2023","unstructured":"Kohzadvand K, Kouhi MM, Barati A, Omrani S, Ghasemi M (2023) Prediction of interfacial wetting behavior of H2\/mineral\/brine; implications for H2 geo-storage. J Energy Storage 72:108567. https:\/\/doi.org\/10.1016\/j.est.2023.108567","journal-title":"J Energy Storage"},{"key":"9821_CR3","unstructured":"Standing M (1947) A pressure-volume-temperature correlation for mixtures of California oils and gases. In: Drilling and production practice. OnePetro"},{"key":"9821_CR4","unstructured":"McCain W, The properties of petroleum fluids, ed,\" ed: Penn Well Books, Penn Well Publishing company, TULSA, Oklahoma, USA. p 1990"},{"key":"9821_CR5","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.cageo.2012.03.016","volume":"44","author":"A Khoukhi","year":"2012","unstructured":"Khoukhi A (2012) Hybrid soft computing systems for reservoir PVT properties prediction. Comput Geosci 44:109\u2013119. https:\/\/doi.org\/10.1016\/j.cageo.2012.03.016","journal-title":"Comput Geosci"},{"key":"9821_CR6","doi-asserted-by":"crossref","unstructured":"Osman E, Abdel-Wahhab O, Al-Marhoun M (2001) Prediction of oil PVT properties using neural networks. In: SPE middle east oil show. OnePetro","DOI":"10.2523\/68233-MS"},{"key":"9821_CR7","doi-asserted-by":"crossref","unstructured":"Ayoub Mohammed MA, Alakbari FS, Nathan CP, Mohyaldinn ME (2022) Determination of the gas\u2013oil ratio below the bubble point pressure using the adaptive neuro-fuzzy inference system (ANFIS). ACS Omega","DOI":"10.1021\/acsomega.2c01496"},{"key":"9821_CR8","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1016\/j.jngse.2015.03.022","volume":"24","author":"H Baniasadi","year":"2015","unstructured":"Baniasadi H, Kamari A, Heidararabi S, Mohammadi AH, Hemmati-Sarapardeh A (2015) Rapid method for the determination of solution gas-oil ratios of petroleum reservoir fluids. J Nat Gas Sci Eng 24:500\u2013509. https:\/\/doi.org\/10.1016\/j.jngse.2015.03.022","journal-title":"J Nat Gas Sci Eng"},{"key":"9821_CR9","doi-asserted-by":"publisher","unstructured":"Vazquez M, Beggs HD (1977) Correlations for fluid physical property prediction. In: SPE annual fall technical conference and exhibition. OnePetro. https:\/\/doi.org\/10.2118\/6719-PA","DOI":"10.2118\/6719-PA"},{"issue":"01","key":"9821_CR10","doi-asserted-by":"publisher","first-page":"41","DOI":"10.2118\/20989-PA","volume":"7","author":"ME Dokla","year":"1992","unstructured":"Dokla ME, Osman ME (1992) Correlation of PVT properties for UAE crudes. SPE Form Eval 7(01):41\u201346. https:\/\/doi.org\/10.2118\/20989-PA","journal-title":"SPE Form Eval"},{"key":"9821_CR11","doi-asserted-by":"publisher","unstructured":"Dokla ME, Osman ME (1991) Correlation of PVT properties for UAE crudes. In: Middle east oil show. OnePetro. https:\/\/doi.org\/10.2118\/21342-MS","DOI":"10.2118\/21342-MS"},{"key":"9821_CR12","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.cjche.2020.08.034","volume":"34","author":"MS Sharafi","year":"2021","unstructured":"Sharafi MS, Ghasemi M, Ahmadi M, Kazemi A (2021) An experimental approach for measuring carbon dioxide diffusion coefficient in water and oil under supercritical conditions. Chin J Chem Eng 34:160\u2013170. https:\/\/doi.org\/10.1016\/j.cjche.2020.08.034","journal-title":"Chin J Chem Eng"},{"issue":"5","key":"9821_CR13","doi-asserted-by":"publisher","first-page":"2579","DOI":"10.1002\/cjce.24675","volume":"101","author":"M Ghasemi","year":"2023","unstructured":"Ghasemi M, Tatar A, Shafiei A, Ivakhnenko OP (2023) Prediction of asphaltene adsorption capacity of clay minerals using machine learning. Can J Chem Eng 101(5):2579\u20132597. https:\/\/doi.org\/10.1002\/cjce.24675","journal-title":"Can J Chem Eng"},{"key":"9821_CR14","doi-asserted-by":"publisher","first-page":"154882","DOI":"10.1016\/j.apsusc.2022.154882","volume":"606","author":"M Ghasemi","year":"2022","unstructured":"Ghasemi M, Shafiei A (2022) Influence of brine compositions on wetting preference of montmorillonite in rock\/brine\/oil system: an in silico study. Appl Surf Sci 606:154882. https:\/\/doi.org\/10.1016\/j.apsusc.2022.154882","journal-title":"Appl Surf Sci"},{"issue":"4","key":"9821_CR15","doi-asserted-by":"publisher","first-page":"1329","DOI":"10.1177\/14759217211029201","volume":"21","author":"W Zhang","year":"2021","unstructured":"Zhang W, Li X (2021) Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions. Struct Health Monit 21(4):1329\u20131344. https:\/\/doi.org\/10.1177\/14759217211029201","journal-title":"Struct Health Monit"},{"issue":"1","key":"9821_CR16","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1109\/TII.2023.3262854","volume":"20","author":"X Li","year":"2024","unstructured":"Li X, Yu S, Lei Y, Li N, Yang B (2024) Intelligent machinery fault diagnosis with event-based camera. IEEE Trans Ind Inf 20(1):380\u2013389. https:\/\/doi.org\/10.1109\/TII.2023.3262854","journal-title":"IEEE Trans Ind Inf"},{"issue":"05","key":"9821_CR17","doi-asserted-by":"publisher","first-page":"785","DOI":"10.2118\/8016-PA","volume":"32","author":"O Glaso","year":"1980","unstructured":"Glaso O (1980) Generalized pressure-volume-temperature correlations. J Pet Technol 32(05):785\u2013795","journal-title":"J Pet Technol"},{"issue":"05","key":"9821_CR18","doi-asserted-by":"publisher","first-page":"650","DOI":"10.2118\/13718-PA","volume":"40","author":"MA Al-Marhoun","year":"1988","unstructured":"Al-Marhoun MA (1988) PVT correlations for Middle East crude oils. J Pet Technol 40(05):650\u2013666","journal-title":"J Pet Technol"},{"key":"9821_CR19","doi-asserted-by":"publisher","unstructured":"Vazquez M, Beggs H (1980) Correlations for fluid physical property prediction. JPT 32(6):968\u2013970. SPE-6719-PA. https:\/\/doi.org\/10.2118\/6719-PA","DOI":"10.2118\/6719-PA"},{"key":"9821_CR20","doi-asserted-by":"crossref","unstructured":"Petrosky G, Farshad F (1993) Pressure-volume-temperature correlations for Gulf of Mexico crude oils. In: SPE annual technical conference and exhibition. OnePetro","DOI":"10.2118\/26644-MS"},{"key":"9821_CR21","unstructured":"Mazandarani MT, Asghari SM (2007) Correlations for predicting solution gas-oil ratio, bubblepoint pressure and oil formation volume factor at bubblepoint of Iran crude oils. In: European congress of chemical engineering, Copenhagen"},{"key":"9821_CR22","unstructured":"Hemmati M, Kharat R (2007) Evaluation of empirically derived PVT properties for Middle East crude oils"},{"key":"9821_CR23","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s00521-016-2793-7","volume":"29","author":"A Daryasafar","year":"2018","unstructured":"Daryasafar A, Daryasafar N, Madani M, Kalantari Meybodi M, Joukar M (2018) Connectionist approaches for solubility prediction of n-alkanes in supercritical carbon dioxide. Neural Comput Appl 29:295\u2013305. https:\/\/doi.org\/10.1007\/s00521-016-2793-7","journal-title":"Neural Comput Appl"},{"key":"9821_CR24","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1016\/j.fuel.2013.01.056","volume":"108","author":"A Shafiei","year":"2013","unstructured":"Shafiei A, Dusseault MB, Zendehboudi S, Chatzis I (2013) A new screening tool for evaluation of steamflooding performance in Naturally Fractured Carbonate Reservoirs. Fuel 108:502\u2013514. https:\/\/doi.org\/10.1016\/j.fuel.2013.01.056","journal-title":"Fuel"},{"issue":"2","key":"9821_CR25","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1016\/j.asoc.2012.10.009","volume":"13","author":"MA Ahmadi","year":"2013","unstructured":"Ahmadi MA, Ebadi M, Shokrollahi A, Majidi SMJ (2013) Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl Soft Comput 13(2):1085\u20131098. https:\/\/doi.org\/10.1016\/j.asoc.2012.10.009","journal-title":"Appl Soft Comput"},{"key":"9821_CR26","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1007\/s00521-015-2088-4","volume":"28","author":"T Helmy","year":"2017","unstructured":"Helmy T et al (2017) Prediction of non-hydrocarbon gas components in separator by using hybrid computational intelligence models. Neural Comput Appl 28:635\u2013649. https:\/\/doi.org\/10.1007\/s00521-015-2088-4","journal-title":"Neural Comput Appl"},{"key":"9821_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.fuel.2020.119146","volume":"285","author":"H Rahmanifard","year":"2021","unstructured":"Rahmanifard H, Maroufi P, Alimohamadi H, Plaksina T, Gates I (2021) The application of supervised machine learning techniques for multivariate modelling of gas component viscosity: a comparative study. Fuel 285:119146. https:\/\/doi.org\/10.1016\/j.fuel.2020.119146","journal-title":"Fuel"},{"key":"9821_CR28","doi-asserted-by":"publisher","first-page":"10111","DOI":"10.1007\/s00521-021-05775-4","volume":"33","author":"M Hemmat Esfe","year":"2021","unstructured":"Hemmat Esfe M, Saedodin S, Bahiraei M, Esfandeh S (2021) Preliminary feasibility study on using a nano-composition in enhanced oil recovery process: neural network modeling. Neural Comput Appl 33:10111\u201310127. https:\/\/doi.org\/10.1007\/s00521-021-05775-4","journal-title":"Neural Comput Appl"},{"key":"9821_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2022.111046","volume":"219","author":"A Shafiei","year":"2022","unstructured":"Shafiei A, Tatar A, Rayhani M, Kairat M, Askarova I (2022) Artificial neural network, support vector machine, decision tree, random forest, and committee machine intelligent system help to improve performance prediction of low salinity water injection in carbonate oil reservoirs. J Pet Sci Eng 219:111046. https:\/\/doi.org\/10.1016\/j.petrol.2022.111046","journal-title":"J Pet Sci Eng"},{"key":"9821_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07682-8","author":"ME Ahmed","year":"2022","unstructured":"Ahmed ME, Sultan AS, Hassan A, Abdulraheem A, Mahmoud M (2022) Predicting the performance of constant volume depletion tests for gas condensate reservoirs using artificial intelligence techniques. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-022-07682-8","journal-title":"Neural Comput Appl"},{"key":"9821_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-023-08256-y","author":"MS Lashkenari","year":"2023","unstructured":"Lashkenari MS, Bagheri M, Tatar A, Rezazadeh H, Inc M (2023) A further study in the prediction of viscosity for Iranian crude oil reservoirs by utilizing a robust radial basis function (RBF) neural network model. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-023-08256-y","journal-title":"Neural Comput Appl"},{"key":"9821_CR32","doi-asserted-by":"publisher","first-page":"2497","DOI":"10.1007\/s00521-020-05158-1","volume":"33","author":"S Kalam","year":"2021","unstructured":"Kalam S, Abu-Khamsin SA, Al-Yousef HY, Gajbhiye R (2021) A novel empirical correlation for waterflooding performance prediction in stratified reservoirs using artificial intelligence. Neural Comput Appl 33:2497\u20132514. https:\/\/doi.org\/10.1007\/s00521-020-05158-1","journal-title":"Neural Comput Appl"},{"key":"9821_CR33","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.jngse.2015.04.008","volume":"25","author":"HA Zamani","year":"2015","unstructured":"Zamani HA, Rafiee-Taghanaki S, Karimi M, Arabloo M, Dadashi A (2015) Implementing ANFIS for prediction of reservoir oil solution gas-oil ratio. J Natural Gas Sci Eng 25:325\u2013334","journal-title":"J Natural Gas Sci Eng"},{"issue":"07","key":"9821_CR34","first-page":"373","volume":"2","author":"SO Baarimah","year":"2015","unstructured":"Baarimah SO, Gawish AA, BinMerdhah AB (2015) Artificial intelligence techniques for predicting the reservoir fluid properties of crude oil systems. Int Res J Eng Technol (IRJET) 2(07):373\u2013382","journal-title":"Int Res J Eng Technol (IRJET)"},{"key":"9821_CR35","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1016\/j.jngse.2016.01.010","volume":"29","author":"S-M Tohidi-Hosseini","year":"2016","unstructured":"Tohidi-Hosseini S-M, Hajirezaie S, Hashemi-Doulatabadi M, Hemmati-Sarapardeh A, Mohammadi AH (2016) Toward prediction of petroleum reservoir fluids properties: a rigorous model for estimation of solution gas-oil ratio. J Natural Gas Sci Eng 29:506\u2013516","journal-title":"J Natural Gas Sci Eng"},{"issue":"308","key":"9821_CR36","first-page":"2","volume":"7","author":"A Kamari","year":"2016","unstructured":"Kamari A, Zendehboudi S, Sheng J, Mohammadi A, Ramjugernath D (2016) Rigorous modeling of solution gas\u2013oil ratios for a wide ranges of reservoir fluid properties. J Pet Environ Biotechnol 7(308):2","journal-title":"J Pet Environ Biotechnol"},{"issue":"1","key":"9821_CR37","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.petlm.2018.12.002","volume":"6","author":"AH Fath","year":"2020","unstructured":"Fath AH, Madanifar F, Abbasi M (2020) Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems. Petroleum 6(1):80\u201391","journal-title":"Petroleum"},{"issue":"23","key":"9821_CR38","doi-asserted-by":"publisher","first-page":"2302","DOI":"10.1080\/10916466.2018.1490759","volume":"37","author":"A Cheshmeh Sefidi","year":"2019","unstructured":"Cheshmeh Sefidi A, Ajorkaran F (2019) A novel MLP-ANN approach to predict solution gas-oil ratio. Pet Sci Technol 37(23):2302\u20132308. https:\/\/doi.org\/10.1080\/10916466.2018.1490759","journal-title":"Pet Sci Technol"},{"key":"9821_CR39","doi-asserted-by":"crossref","unstructured":"Makinde I (2019) A new way to forecast gas-oil ratios GOR and solution gas production from unconventional oil reservoirs. In: SPE liquids-rich basins conference-North America. OnePetro","DOI":"10.2118\/197096-MS"},{"key":"9821_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijft.2022.100159","volume":"14","author":"R Abdel-Azim","year":"2022","unstructured":"Abdel-Azim R (2022) Estimation of bubble point pressure and solution gas oil ratio using artificial neural network. Int J Thermofluids 14:100159","journal-title":"Int J Thermofluids"},{"key":"9821_CR41","doi-asserted-by":"crossref","unstructured":"Gharbi RB, Elsharkawy AM (1997) Universal neural network based model for estimating the PVT properties of crude oil systems. In: SPE Asia Pacific oil and gas conference and exhibition. OnePetro","DOI":"10.2523\/38099-MS"},{"key":"9821_CR42","doi-asserted-by":"crossref","unstructured":"Elsharkawy AM (1998) Modeling the properties of crude oil and gas systems using RBF network. In: SPE Asia Pacific oil and gas conference and exhibition. OnePetro","DOI":"10.2118\/49961-MS"},{"issue":"2","key":"9821_CR43","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1021\/ef980143v","volume":"13","author":"RB Gharbi","year":"1999","unstructured":"Gharbi RB, Elsharkawy AM, Karkoub M (1999) Universal neural-network-based model for estimating the PVT properties of crude oil systems. Energy Fuels 13(2):454\u2013458","journal-title":"Energy Fuels"},{"issue":"5\u20136","key":"9821_CR44","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1080\/10916469908949738","volume":"17","author":"F Boukadi","year":"1999","unstructured":"Boukadi F, Al-Alawi S, Al-Bemani A, Al-Qassabi S (1999) Establishing PVT correlations for Omani oils. Pet Sci Technol 17(5\u20136):637\u2013662","journal-title":"Pet Sci Technol"},{"key":"9821_CR45","doi-asserted-by":"crossref","unstructured":"Abdel-Aal R (2002) Abductive networks: a new modeling tool for the oil and gas industry. In: SPE Asia Pacific oil and gas conference and exhibition. OnePetro","DOI":"10.2523\/77882-MS"},{"key":"9821_CR46","doi-asserted-by":"crossref","unstructured":"Al-Marhoun M, Osman E (2002) Using artificial neural networks to develop new PVT correlations for Saudi crude oils. In: Abu Dhabi international petroleum exhibition and conference. OnePetro","DOI":"10.2523\/78592-MS"},{"key":"9821_CR47","doi-asserted-by":"crossref","unstructured":"Goda HM, El-M Shokir EM, Fattah KA, Sayyouh MH (2003) Prediction of the PVT data using neural network computing theory. In: Nigeria annual international conference and exhibition. OnePetro","DOI":"10.2523\/85650-MS"},{"issue":"2","key":"9821_CR48","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1021\/ef0501750","volume":"20","author":"A Malallah","year":"2006","unstructured":"Malallah A, Gharbi R, Algharaib M (2006) Accurate estimation of the world crude oil PVT properties using graphical alternating conditional expectation. Energy Fuels 20(2):688\u2013698","journal-title":"Energy Fuels"},{"key":"9821_CR49","doi-asserted-by":"crossref","unstructured":"El-Sebakhy EA, Sheltami T, Al-Bokhitan SY, Shaaban Y, Raharja PD, Khaeruzzaman Y (2007) Support vector machines framework for predicting the PVT properties of crude oil systems. In: SPE Middle East oil and gas show and conference. OnePetro","DOI":"10.2523\/105698-MS"},{"issue":"1\u20134","key":"9821_CR50","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.petrol.2008.12.006","volume":"64","author":"EA El-Sebakhy","year":"2009","unstructured":"El-Sebakhy EA (2009) Forecasting PVT properties of crude oil systems based on support vector machines modeling scheme. J Pet Sci Eng 64(1\u20134):25\u201334","journal-title":"J Pet Sci Eng"},{"issue":"1\u20132","key":"9821_CR51","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.petrol.2010.03.007","volume":"72","author":"S Dutta","year":"2010","unstructured":"Dutta S, Gupta J (2010) PVT correlations for Indian crude using artificial neural networks. J Pet Sci Eng 72(1\u20132):93\u2013109","journal-title":"J Pet Sci Eng"},{"issue":"2","key":"9821_CR52","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.petrol.2011.06.024","volume":"78","author":"J Asadisaghandi","year":"2011","unstructured":"Asadisaghandi J, Tahmasebi P (2011) Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields. J Pet Sci Eng 78(2):464\u2013475","journal-title":"J Pet Sci Eng"},{"issue":"10","key":"9821_CR53","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1080\/10916460903551040","volume":"29","author":"JN Moghadam","year":"2011","unstructured":"Moghadam JN, Salahshoor K, Kharrat R (2011) Introducing a new method for predicting PVT properties of Iranian crude oils by applying artificial neural networks. Pet Sci Technol 29(10):1066\u20131079","journal-title":"Pet Sci Technol"},{"key":"9821_CR54","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.fluid.2013.02.012","volume":"346","author":"S Rafiee-Taghanaki","year":"2013","unstructured":"Rafiee-Taghanaki S, Arabloo M, Chamkalani A, Amani M, Zargari MH, Adelzadeh MR (2013) Implementation of SVM framework to estimate PVT properties of reservoir oil. Fluid Phase Equilib 346:25\u201332","journal-title":"Fluid Phase Equilib"},{"key":"9821_CR55","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.fuproc.2013.06.007","volume":"115","author":"A Farasat","year":"2013","unstructured":"Farasat A, Shokrollahi A, Arabloo M, Gharagheizi F, Mohammadi AH (2013) Toward an intelligent approach for determination of saturation pressure of crude oil. Fuel Process Technol 115:201\u2013214","journal-title":"Fuel Process Technol"},{"key":"9821_CR56","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.jngse.2014.03.010","volume":"18","author":"M Karimnezhad","year":"2014","unstructured":"Karimnezhad M, Heidarian M, Kamari M, Jalalifar H (2014) A new empirical correlation for estimating bubble point oil formation volume factor. J Natural Gas Sci Eng 18:329\u2013335","journal-title":"J Natural Gas Sci Eng"},{"key":"9821_CR57","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.jtice.2015.04.009","volume":"55","author":"A Shokrollahi","year":"2015","unstructured":"Shokrollahi A, Tatar A, Safari H (2015) On accurate determination of PVT properties in crude oil systems: committee machine intelligent system modeling approach. J Taiwan Inst Chem Eng 55:17\u201326","journal-title":"J Taiwan Inst Chem Eng"},{"key":"9821_CR58","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.petrol.2016.05.008","volume":"147","author":"S Salehinia","year":"2016","unstructured":"Salehinia S, Salehinia Y, Alimadadi F, Sadati SH (2016) Forecasting density, oil formation volume factor and bubble point pressure of crude oil systems based on nonlinear system identification approach. J Pet Sci Eng 147:47\u201355","journal-title":"J Pet Sci Eng"},{"key":"9821_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.fuel.2019.116834","volume":"269","author":"M Seyyedattar","year":"2020","unstructured":"Seyyedattar M, Ghiasi MM, Zendehboudi S, Butt S (2020) Determination of bubble point pressure and oil formation volume factor: extra trees compared with LSSVM-CSA hybrid and ANFIS models. Fuel 269:116834","journal-title":"Fuel"},{"key":"9821_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2021.108425","volume":"202","author":"S Rashidi","year":"2021","unstructured":"Rashidi S et al (2021) Determination of bubble point pressure & oil formation volume factor of crude oils applying multiple hidden layers extreme learning machine algorithms. J Pet Sci Eng 202:108425","journal-title":"J Pet Sci Eng"},{"key":"9821_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2021.109410","volume":"208","author":"MA Ayoub","year":"2022","unstructured":"Ayoub MA, Elhadi A, Fatherlhman D, Saleh M, Alakbari FS, Mohyaldinn ME (2022) A new correlation for accurate prediction of oil formation volume factor at the bubble point pressure using Group Method of Data Handling approach. J Pet Sci Eng 208:109410","journal-title":"J Pet Sci Eng"},{"key":"9821_CR62","doi-asserted-by":"crossref","unstructured":"De Ghetto G, Paone F, Villa M (1995) Pressure-volume-temperature correlations for heavy and extra heavy oils. In: SPE international heavy oil symposium. OnePetro","DOI":"10.2118\/30316-MS"},{"key":"9821_CR63","doi-asserted-by":"crossref","unstructured":"De Ghetto G, Villa M (1994) Reliability analysis on PVT correlations. In: European petroleum conference. OnePetro","DOI":"10.2118\/28904-MS"},{"key":"9821_CR64","doi-asserted-by":"crossref","unstructured":"Omar M, Todd A (1993) Development of new modified black oil correlations for Malaysian crudes. In: SPE Asia Pacific oil and gas conference. OnePetro","DOI":"10.2523\/25338-MS"},{"key":"9821_CR65","unstructured":"Khairy M, El-Tayeb S, Hamdallah M (1998) PVT correlations developed for Egyptian crudes. Oil Gas J 96(18)"},{"key":"9821_CR66","doi-asserted-by":"publisher","first-page":"23","DOI":"10.5004\/dwt.2020.26063","volume":"200","author":"E Reyes-T\u00e9llez","year":"2020","unstructured":"Reyes-T\u00e9llez E et al (2020) Analysis of transfer functions and normalizations in an ANN model that predicts the transport of energy in a parabolic trough solar collector. Desalin Water Treat 200:23\u201341","journal-title":"Desalin Water Treat"},{"issue":"1","key":"9821_CR67","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12665-018-8028-8","volume":"78","author":"B Keshtegar","year":"2019","unstructured":"Keshtegar B, Heddam S, Hosseinabadi H (2019) The employment of polynomial chaos expansion approach for modeling dissolved oxygen concentration in river. Environ Earth Sci 78(1):1\u201318. https:\/\/doi.org\/10.1007\/s12665-018-8028-8","journal-title":"Environ Earth Sci"},{"issue":"3","key":"9821_CR68","doi-asserted-by":"publisher","first-page":"353","DOI":"10.3390\/polym13030353","volume":"13","author":"K-C Ke","year":"2021","unstructured":"Ke K-C, Huang M-S (2021) Quality classification of injection-molded components by using quality indices, grading, and machine learning. Polymers 13(3):353. https:\/\/doi.org\/10.3390\/polym13030353","journal-title":"Polymers"},{"issue":"12","key":"9821_CR69","doi-asserted-by":"publisher","first-page":"5438","DOI":"10.1109\/TIE.2011.2164773","volume":"58","author":"H Yu","year":"2011","unstructured":"Yu H, Xie T, Paszczy\u00f1ski S, Wilamowski BM (2011) Advantages of radial basis function networks for dynamic system design. IEEE Trans Ind Electron 58(12):5438\u20135450","journal-title":"IEEE Trans Ind Electron"},{"key":"9821_CR70","doi-asserted-by":"crossref","unstructured":"Tin\u00f3s R, J\u00fanior LOM (2009) Use of the q-Gaussian function in radial basis function networks. In: Foundations of computational intelligence volume 5. Springer, pp 127\u2013145","DOI":"10.1007\/978-3-642-01536-6_6"},{"key":"9821_CR71","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neucom.2018.01.046","volume":"286","author":"G Ren","year":"2018","unstructured":"Ren G, Cao Y, Wen S, Huang T, Zeng Z (2018) A modified Elman neural network with a new learning rate scheme. Neurocomputing 286:11\u201318","journal-title":"Neurocomputing"},{"key":"9821_CR72","doi-asserted-by":"crossref","unstructured":"Wysocki A , \u0141awry\u0144czuk M (2016) Elman neural network for modeling and predictive control of delayed dynamic systems. Arch Control Sci 26(1)","DOI":"10.1515\/acsc-2016-0007"},{"key":"9821_CR73","first-page":"465","volume-title":"International symposium on neural networks","author":"Z Zhang","year":"2007","unstructured":"Zhang Z, Tang Z, Tang G, Catherine V, Wang X, Xiong R (2007) An improved algorithm for eleman neural network by adding a modified error function. International symposium on neural networks. Springer, pp 465\u2013473"},{"key":"9821_CR74","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/8374696","author":"C Yu","year":"2022","unstructured":"Yu C (2022) Using Elman neural network model to forecast and analyze the agricultural economy. J Math. https:\/\/doi.org\/10.1155\/2022\/8374696","journal-title":"J Math"},{"key":"9821_CR75","volume-title":"Petroleum production handbook: reservoir engineering","author":"TC Frick","year":"1962","unstructured":"Frick TC (1962) Petroleum production handbook: reservoir engineering. McGraw-Hill"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09821-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09821-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09821-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T08:58:22Z","timestamp":1730105902000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09821-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,8]]},"references-count":75,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["9821"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09821-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,8]]},"assertion":[{"value":"10 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2024","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The original online version of this article was revised to update the first author name.","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}