{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T07:01:48Z","timestamp":1784358108868,"version":"3.55.0"},"reference-count":98,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T00:00:00Z","timestamp":1700265600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T00:00:00Z","timestamp":1700265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s00521-023-09201-9","type":"journal-article","created":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T20:02:26Z","timestamp":1700337746000},"page":"1973-1995","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine learning modelling of dew point pressure in gas condensate reservoirs: application of decision tree-based models"],"prefix":"10.1007","volume":"36","author":[{"given":"Zohre","family":"Esmaeili-Jaghdan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Afshin","family":"Tatar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7938-7022","authenticated-orcid":false,"given":"Amin","family":"Shokrollahi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Bon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abbas","family":"Zeinijahromi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,18]]},"reference":[{"issue":"05","key":"9201_CR1","doi-asserted-by":"publisher","first-page":"596","DOI":"10.2118\/89904-PA","volume":"9","author":"M Bozorgzadeh","year":"2006","unstructured":"Bozorgzadeh M, Gringarten AC (2006) condensate bank characterization from well test data and fluid PVT properties. SPE Reserv Evaluat Eng 9(05):596\u2013611","journal-title":"SPE Reserv Evaluat Eng"},{"key":"9201_CR2","unstructured":"Fevang O (1995) Gas condensate flow behavior and sampling. Division of Petroleum Engineering and Applied Geophysics"},{"issue":"1\u20132","key":"9201_CR3","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1081\/LFT-120016924","volume":"21","author":"S Fasesan","year":"2003","unstructured":"Fasesan S, Olukini O, Adewumi O (2003) Characteristics of gas condensate. Petroleum Sci Technol 21(1\u20132):81\u201390","journal-title":"Petroleum Sci Technol"},{"key":"9201_CR4","volume-title":"Properties of petroleum fluids","author":"WD McCain Jr","year":"1973","unstructured":"McCain WD Jr (1973) Properties of petroleum fluids. Petroleum Publishing Co., Tulsa, OK"},{"key":"9201_CR5","doi-asserted-by":"crossref","unstructured":"El-Banbi AH, McCain WD, Jr., Semmelbeck ME (2000) Investigation of well productivity in gas-condensate reservoirs. SPE\/CERI gas technology symposium","DOI":"10.2118\/59773-MS"},{"issue":"4","key":"9201_CR6","first-page":"14","volume":"17","author":"L Fan","year":"2005","unstructured":"Fan L, Harris BW, Jamaluddin A, Kamath J, Mott R, Pope GA et al (2005) Understanding gas-condensate reservoirs. Oilfield Rev 17(4):14\u201327","journal-title":"Oilfield Rev"},{"key":"9201_CR7","volume-title":"PVT and phase behaviour of petroleum reservoir fluids","author":"A Danesh","year":"1998","unstructured":"Danesh A (1998) PVT and phase behaviour of petroleum reservoir fluids. Elsevier, Amsterdam"},{"key":"9201_CR8","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1016\/j.jngse.2015.09.034","volume":"27","author":"C Li","year":"2015","unstructured":"Li C, Peng Y, Dong J, Chen L (2015) Prediction of the dew point pressure for gas condensate using a modified Peng-Robinson equation of state and a four-coefficient molar distribution function. J Natural Gas Sci Eng 27:967\u2013978. https:\/\/doi.org\/10.1016\/j.jngse.2015.09.034","journal-title":"J Natural Gas Sci Eng"},{"key":"9201_CR9","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.jngse.2013.05.006","volume":"14","author":"R Mokhtari","year":"2013","unstructured":"Mokhtari R, Varzandeh F, Rahimpour MR (2013) Well productivity in an Iranian gas-condensate reservoir: a case study. J Natural Gas Sci Eng 14:66\u201376. https:\/\/doi.org\/10.1016\/j.jngse.2013.05.006","journal-title":"J Natural Gas Sci Eng"},{"key":"9201_CR10","volume-title":"Reservoir engineering handbook","author":"T Ahmed","year":"2018","unstructured":"Ahmed T (2018) Reservoir engineering handbook. Gulf professional publishing, Houston"},{"key":"9201_CR11","volume-title":"Properties of oils and natural gases","author":"KS Pedersen","year":"1989","unstructured":"Pedersen KS, Fredenslund A, Thomassen P (1989) Properties of oils and natural gases. Gulf Publishing Company, Houston"},{"key":"9201_CR12","unstructured":"Doherty L (2000) Memorial fund of AIME, Society of petroleum engineers"},{"key":"9201_CR13","doi-asserted-by":"crossref","unstructured":"Bon J, Bon PJ, Ortega AJ, Nalepa W, Koo R (2017) Design of experimental test method to investigate the effect of OBM contamination on PVT samples from gas condensate reservoirs. In: SPE\/IATMI Asia Pacific Oil & Gas Conference and Exhibition","DOI":"10.2118\/186256-MS"},{"issue":"06","key":"9201_CR14","doi-asserted-by":"publisher","first-page":"589","DOI":"10.2118\/101037-pa","volume":"10","author":"J Bon","year":"2007","unstructured":"Bon J, Sarma H, Rodrigues T, Bon J (2007) Reservoir-fluid sampling revisited\u2014 a practical perspective. SPE Reservoir Eval Eng 10(06):589\u2013596. https:\/\/doi.org\/10.2118\/101037-pa","journal-title":"SPE Reservoir Eval Eng"},{"issue":"1","key":"9201_CR15","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/S0378-3812(01)00724-5","volume":"193","author":"AM Elsharkawy","year":"2002","unstructured":"Elsharkawy AM (2002) Predicting the dew point pressure for gas condensate reservoirs: empirical models and equations of state. Fluid Phase Equilib 193(1):147\u2013165. https:\/\/doi.org\/10.1016\/S0378-3812(01)00724-5","journal-title":"Fluid Phase Equilib"},{"key":"9201_CR16","unstructured":"Eilerts CK, Smith RV (1942) Specific volumes and phase-boundary properties of separator-gas and liquid-hydrocarbon mixtures. US Department of the Interior, Bureau of Mines"},{"issue":"01","key":"9201_CR17","doi-asserted-by":"publisher","first-page":"287","DOI":"10.2118\/949287-G","volume":"179","author":"RH Olds","year":"1949","unstructured":"Olds RH, Sage BH, Lacey WN (1949) Volumetric and viscosity studies of oil and gas from a san joaquin valley field. Trans AIME 179(01):287\u2013302. https:\/\/doi.org\/10.2118\/949287-G","journal-title":"Trans AIME"},{"issue":"5","key":"9201_CR18","doi-asserted-by":"publisher","first-page":"135","DOI":"10.2118\/140-G","volume":"4","author":"EI Organick","year":"1952","unstructured":"Organick EI, Golding BH (1952) Prediction of saturation pressures for condensate-gas and volatile-oil mixtures. J Petrol Technol 4(5):135\u2013148. https:\/\/doi.org\/10.2118\/140-G","journal-title":"J Petrol Technol"},{"key":"9201_CR19","volume-title":"A correlation of dew-point pressure with reservoir fluid composition and temperature","author":"LK Nemeth","year":"1966","unstructured":"Nemeth LK (1966) A correlation of dew-point pressure with reservoir fluid composition and temperature. Texas A&M University, Libraries"},{"issue":"2","key":"9201_CR20","doi-asserted-by":"publisher","first-page":"99","DOI":"10.2118\/1477-PA","volume":"7","author":"LK Nemeth","year":"1967","unstructured":"Nemeth LK, Kennedy HT (1967) A correlation of dewpoint pressure with fluid composition and temperature. Soci Petrol Eng J 7(2):99\u2013104. https:\/\/doi.org\/10.2118\/1477-PA","journal-title":"Soci Petrol Eng J"},{"key":"9201_CR21","doi-asserted-by":"crossref","unstructured":"Potsch KT, Braeuer L (1996) A Novel Graphical Method for Determining Dewpoint Pressures of Gas Condensates. In: European petroleum conference. Milan, Italy: Society of Petroleum Engineers, p. 3.","DOI":"10.2118\/36919-MS"},{"key":"9201_CR22","volume-title":"Improved correlations for retrograde gases","author":"A Crogh","year":"1996","unstructured":"Crogh A (1996) Improved correlations for retrograde gases. Texas A&M University, Texas"},{"key":"9201_CR23","unstructured":"Fang Y, Li B, Hu Y, Sun Z, Zhu Y (1988) Condensate gas phase behavior and development. In: SPE International oil and gas conference and exhibition in China. Society of Petroleum Engineers, Beijing, China, p. 20"},{"key":"9201_CR24","doi-asserted-by":"crossref","unstructured":"Elsharkawy AM (2001) Characterization of the plus fraction and prediction of the dewpoint pressure for gas condensate reservoirs. In: SPE Western regional meeting. Society of Petroleum Engineers, Bakersfield, California, p. 18","DOI":"10.2118\/68776-MS"},{"key":"9201_CR25","doi-asserted-by":"crossref","unstructured":"Humoud AA, Al-Marhoun MA (2001) A new correlation for gas-condensate dewpoint pressure prediction. In: SPE Middle east oil show. Manama, Bahrain: Society of petroleum engineers, p. 8","DOI":"10.2118\/68230-MS"},{"key":"9201_CR26","doi-asserted-by":"crossref","unstructured":"Marruffo I, Maita J, Him J, Gonzalo R (2002) Correlations to determine retrograde dew pressure and C7+ percentage of gas condensate reservoirs on basis of production test data of eastern venezuelan fields. In: SPE gas technology symposium. Society of Petroleum Engineers, Calgary, Alberta, Canada, p. 6","DOI":"10.2118\/75686-MS"},{"key":"9201_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jngse.2021.104134","volume":"94","author":"J Kim","year":"2021","unstructured":"Kim J, Chae M, Han J, Park S, Lee Y (2021) The development of leak detection model in subsea gas pipeline using machine learning. J Nat Gas Sci Eng 94:104134. https:\/\/doi.org\/10.1016\/j.jngse.2021.104134","journal-title":"J Nat Gas Sci Eng"},{"key":"9201_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.fuel.2022.123282","volume":"315","author":"Z Tariq","year":"2022","unstructured":"Tariq Z, Murtaza M, Mahmoud M, Aljawad MS, Kamal MS (2022) Machine learning approach to predict the dynamic linear swelling of shales treated with different waterbased drilling fluids. Fuel 315:123282. https:\/\/doi.org\/10.1016\/j.fuel.2022.123282","journal-title":"Fuel"},{"issue":"85","key":"9201_CR29","doi-asserted-by":"publisher","first-page":"35999","DOI":"10.1016\/j.ijhydene.2022.08.195","volume":"47","author":"A Tatar","year":"2022","unstructured":"Tatar A, Esmaeili-Jaghdan Z, Shokrollahi A, Zeinijahromi A (2022) Hydrogen solubility in n-alkanes: data mining and modelling with machine learning approach. Int J Hydrogen Energy 47(85):35999\u201336021. https:\/\/doi.org\/10.1016\/j.ijhydene.2022.08.195","journal-title":"Int J Hydrogen Energy"},{"key":"9201_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.geoen.2023.211778","volume":"226","author":"M Rayhani","year":"2023","unstructured":"Rayhani M, Tatar A, Shokrollahi A, Zeinijahromi A (2023) Exploring the power of machine learning in analyzing the gas minimum miscibility pressure in hydrocarbons. Geoenergy Sci Eng 226:211778. https:\/\/doi.org\/10.1016\/j.geoen.2023.211778","journal-title":"Geoenergy Sci Eng"},{"issue":"3","key":"9201_CR31","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/S0920-4105(02)00352-2","volume":"37","author":"A Gonz\u00e1lez","year":"2003","unstructured":"Gonz\u00e1lez A, Barrufet MA, Startzman R (2003) Improved neural-network model predicts dewpoint pressure of retrograde gases. J Petrol Sci Eng 37(3):183\u2013194. https:\/\/doi.org\/10.1016\/S0920-4105(02)00352-2","journal-title":"J Petrol Sci Eng"},{"key":"9201_CR32","unstructured":"Jalali F, Abdy Y, Akbari MK (2007) Using artificial neural network's capability for estimation of gas condensate reservoir's dew point pressure. In: EUROPEC\/EAGE conference and exhibition. London, UK, Society of Petroleum Engineers, p. 10."},{"key":"9201_CR33","doi-asserted-by":"crossref","unstructured":"Al-Dhamen M, Al-Marhoun M (2011) New correlations for dew-point pressure for gas condensate. In: SPE Saudi Arabia section young professionals technical symposium. Society of Petroleum Engineers, Dhahran, Saudi Arabia. p. 18.","DOI":"10.2118\/155410-MS"},{"key":"9201_CR34","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1016\/j.fuel.2013.10.010","volume":"117","author":"MA Ahmadi","year":"2014","unstructured":"Ahmadi MA, Ebadi M (2014) Evolving smart approach for determination dew point pressure through condensate gas reservoirs. Fuel 117:1074\u20131084. https:\/\/doi.org\/10.1016\/j.fuel.2013.10.010","journal-title":"Fuel"},{"issue":"3","key":"9201_CR35","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.petlm.2016.05.001","volume":"3","author":"MA Ahmadi","year":"2017","unstructured":"Ahmadi MA, Elsharkawy A (2017) Robust correlation to predict dew point pressure of gas condensate reservoirs. Petroleum 3(3):340\u2013347. https:\/\/doi.org\/10.1016\/j.petlm.2016.05.001","journal-title":"Petroleum"},{"issue":"9","key":"9201_CR36","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1080\/10916466.2020.1769655","volume":"38","author":"A Ali","year":"2020","unstructured":"Ali A, Guo L (2020) Adaptive neuro-fuzzy approach for prediction of dewpoint pressure for gas condensate reservoirs. Pet Sci Technol 38(9):673\u2013681. https:\/\/doi.org\/10.1080\/10916466.2020.1769655","journal-title":"Pet Sci Technol"},{"key":"9201_CR37","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.fuproc.2013.07.005","volume":"116","author":"M Arabloo","year":"2013","unstructured":"Arabloo M, Shokrollahi A, Gharagheizi F, Mohammadi AH (2013) Toward a predictive model for estimating dew point pressure in gas condensate systems. Fuel Process Technol 116:317\u2013324. https:\/\/doi.org\/10.1016\/j.fuproc.2013.07.005","journal-title":"Fuel Process Technol"},{"issue":"10","key":"9201_CR38","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1080\/10916466.2020.1780257","volume":"38","author":"R Daneshfar","year":"2020","unstructured":"Daneshfar R, Keivanimehr F, Mohammadi-Khanaposhtani M, Baghban A (2020) A neural computing strategy to estimate dew-point pressure of gas condensate reservoirs. Pet Sci Technol 38(10):706\u2013712. https:\/\/doi.org\/10.1080\/10916466.2020.1780257","journal-title":"Pet Sci Technol"},{"issue":"5","key":"9201_CR39","volume":"2","author":"AN El-hoshoudy","year":"2018","unstructured":"El-hoshoudy AN, Gomaa S, Desouky SM (2018) Prediction of dew point pressure in gas condensate reservoirs based on a combination of gene expression programming (GEP) and multiple regression analysis. Petrol Petrochem Eng J 2(5):000163","journal-title":"Petrol Petrochem Eng J"},{"issue":"24","key":"9201_CR40","doi-asserted-by":"publisher","first-page":"2969","DOI":"10.1080\/10916466.2014.919004","volume":"32","author":"S Ghassemzadeh","year":"2014","unstructured":"Ghassemzadeh S, Schaffie M, Sarrafi A, Ranjbar M (2014) Predicting dew point pressure: using a hybrid intelligent network. Pet Sci Technol 32(24):2969\u20132975. https:\/\/doi.org\/10.1080\/10916466.2014.919004","journal-title":"Pet Sci Technol"},{"issue":"10","key":"9201_CR41","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1080\/10916466.2011.598895","volume":"31","author":"S Ghassemzadeh","year":"2013","unstructured":"Ghassemzadeh S, Shafflie M, Sarrafi A, Ranjbar M (2013) The importance of normalization in predicting dew point pressure by ANFIS. Pet Sci Technol 31(10):1040\u20131047. https:\/\/doi.org\/10.1080\/10916466.2011.598895","journal-title":"Pet Sci Technol"},{"key":"9201_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2019.106558","volume":"184","author":"M Haji-Savameri","year":"2020","unstructured":"Haji-Savameri M, Menad NA, Norouzi-Apourvari S, Hemmati-Sarapardeh A (2020) Modeling dew point pressure of gas condensate reservoirs: comparison of hybrid soft computing approaches, correlations, and thermodynamic models. J Petrol Sci Eng 184:106558. https:\/\/doi.org\/10.1016\/j.petrol.2019.106558","journal-title":"J Petrol Sci Eng"},{"key":"9201_CR43","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.jtice.2015.10.011","volume":"60","author":"A Kamari","year":"2016","unstructured":"Kamari A, Sattari M, Mohammadi AH, Ramjugernath D (2016) Rapid method for the estimation of dew point pressures in gas condensate reservoirs. J Taiwan Inst Chem Eng 60:258\u2013266. https:\/\/doi.org\/10.1016\/j.jtice.2015.10.011","journal-title":"J Taiwan Inst Chem Eng"},{"issue":"12","key":"9201_CR44","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1080\/10916466.2010.540616","volume":"31","author":"H Kaydani","year":"2013","unstructured":"Kaydani H, Hagizadeh A, Mohebbi A (2013) A dew point pressure model for gas condensate reservoirs based on an artificial neural network. Pet Sci Technol 31(12):1228\u20131237. https:\/\/doi.org\/10.1080\/10916466.2010.540616","journal-title":"Pet Sci Technol"},{"key":"9201_CR45","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.asoc.2016.05.049","volume":"47","author":"H Kaydani","year":"2016","unstructured":"Kaydani H, Mohebbi A, Hajizadeh A (2016) Dew point pressure model for gas condensate reservoirs based on multi-gene genetic programming approach. Appl Soft Comput 47:168\u2013178. https:\/\/doi.org\/10.1016\/j.asoc.2016.05.049","journal-title":"Appl Soft Comput"},{"issue":"3","key":"9201_CR46","doi-asserted-by":"publisher","DOI":"10.1115\/1.4032226","volume":"138","author":"A Khaksar Manshad","year":"2016","unstructured":"Khaksar Manshad A, Rostami H, Moein Hosseini S, Rezaei H (2016) Application of artificial neural network-particle swarm optimization algorithm for prediction of gas condensate dew point pressure and comparison with gaussian processes regression-particle swarm optimization algorithm. J Energy Res Technol 138(3):032903. https:\/\/doi.org\/10.1115\/1.4032226","journal-title":"J Energy Res Technol"},{"key":"9201_CR47","doi-asserted-by":"crossref","unstructured":"Khan MR, Kalam S, Tariq Z, Abdulraheem A (2019) A novel empirical correlation to predict the dew point pressure using intelligent algorithms. In: Abu Dhabi international petroleum exhibition & conference. Abu Dhabi, UAE: Society of Petroleum Engineers, p. 15","DOI":"10.2118\/197951-MS"},{"issue":"5","key":"9201_CR48","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1016\/j.cherd.2013.08.014","volume":"92","author":"SMJ Majidi","year":"2014","unstructured":"Majidi SMJ, Shokrollahi A, Arabloo M, Mahdikhani-Soleymanloo R, Masihi M (2014) Evolving an accurate model based on machine learning approach for prediction of dew-point pressure in gas condensate reservoirs. Chem Eng Res Des 92(5):891\u2013902. https:\/\/doi.org\/10.1016\/j.cherd.2013.08.014","journal-title":"Chem Eng Res Des"},{"key":"9201_CR49","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1016\/j.molliq.2016.08.087","volume":"223","author":"A Najafi-Marghmaleki","year":"2016","unstructured":"Najafi-Marghmaleki A, Tatar A, Barati-Harooni A, Choobineh M-J, Mohammadi AH (2016) GA-RBF model for prediction of dew point pressure in gas condensate reservoirs. J Mol Liq 223:979\u2013986. https:\/\/doi.org\/10.1016\/j.molliq.2016.08.087","journal-title":"J Mol Liq"},{"issue":"3","key":"9201_CR50","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1016\/j.fuproc.2008.11.009","volume":"90","author":"S Nowroozi","year":"2009","unstructured":"Nowroozi S, Ranjbar M, Hashemipour H, Schaffie M (2009) Development of a neural fuzzy system for advanced prediction of dew point pressure in gas condensate reservoirs. Fuel Process Technol 90(3):452\u2013457. https:\/\/doi.org\/10.1016\/j.fuproc.2008.11.009","journal-title":"Fuel Process Technol"},{"key":"9201_CR51","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.fluid.2014.11.027","volume":"387","author":"A Rabiei","year":"2015","unstructured":"Rabiei A, Sayyad H, Riazi M, Hashemi A (2015) Determination of dew point pressure in gas condensate reservoirs based on a hybrid neural genetic algorithm. Fluid Phase Equilib 387:38\u201349. https:\/\/doi.org\/10.1016\/j.fluid.2014.11.027","journal-title":"Fluid Phase Equilib"},{"issue":"3","key":"9201_CR52","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1007\/s00521-012-1275-9","volume":"24","author":"H Rostami","year":"2014","unstructured":"Rostami H, Khaksar MA (2014) Application of evolutionary Gaussian processes regression by particle swarm optimization for prediction of dew point pressure in gas condensate reservoirs. Neural Comput Appl 24(3):705\u2013713. https:\/\/doi.org\/10.1007\/s00521-012-1275-9","journal-title":"Neural Comput Appl"},{"key":"9201_CR53","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.jngse.2014.02.009","volume":"18","author":"H Rostami-Hosseinkhani","year":"2014","unstructured":"Rostami-Hosseinkhani H, Esmaeilzadeh F, Mowla D (2014) Application of expert systems for accurate determination of dew-point pressure of gas condensate reservoirs. J Natural Gas Sci Eng 18:296\u2013303. https:\/\/doi.org\/10.1016\/j.jngse.2014.02.009","journal-title":"J Natural Gas Sci Eng"},{"issue":"5","key":"9201_CR54","doi-asserted-by":"publisher","first-page":"3194","DOI":"10.1021\/ef800225b","volume":"22","author":"EME-M Shokir","year":"2008","unstructured":"Shokir EME-M (2008) Dewpoint pressure model for gas condensate reservoirs based on genetic programming. Energy Fuels 22(5):3194\u20133200. https:\/\/doi.org\/10.1021\/ef800225b","journal-title":"Energy Fuels"},{"key":"9201_CR55","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1016\/j.fuel.2018.05.168","volume":"232","author":"Z Zhong","year":"2018","unstructured":"Zhong Z, Liu S, Kazemi M, Carr TR (2018) Dew point pressure prediction based on mixed-kernels-function support vector machine in gas-condensate reservoir. Fuel 232:600\u2013609. https:\/\/doi.org\/10.1016\/j.fuel.2018.05.168","journal-title":"Fuel"},{"key":"9201_CR56","doi-asserted-by":"crossref","unstructured":"Godwin ON (2012) A new analytical method for predicting dew point pressures for gas condensate reservoirs. In: Nigeria annual international conference and exhibition. Lagos, Nigeria: Society of Petroleum Engineers, p. 11","DOI":"10.2118\/162985-MS"},{"issue":"4","key":"9201_CR57","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1007\/s13202-016-0308-9","volume":"7","author":"A Alzahabi","year":"2017","unstructured":"Alzahabi A, El-Banbi A, Alexandre Trindade A, Soliman M (2017) A regression model for estimation of dew point pressure from down-hole fluid analyzer data. J Petrol Explorat Prod Technol 7(4):1173\u20131183. https:\/\/doi.org\/10.1007\/s13202-016-0308-9","journal-title":"J Petrol Explorat Prod Technol"},{"key":"9201_CR58","doi-asserted-by":"publisher","first-page":"110072","DOI":"10.1016\/j.petrol.2021.110072","volume":"2021","author":"M Mirzaie","year":"2021","unstructured":"Mirzaie M, Esfandyari H, Tatar A (2021) Dew point pressure of gas condensates, modeling and a comprehensive review on literature data. J Petrol Sci Eng 2021:110072. https:\/\/doi.org\/10.1016\/j.petrol.2021.110072","journal-title":"J Petrol Sci Eng"},{"key":"9201_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2021.109284","volume":"208","author":"A Gouda","year":"2022","unstructured":"Gouda A, Gomaa S, Attia A, Emara R, Desouky SM, El-hoshoudy AN (2022) Development of an artificial neural network model for predicting the dew point pressure of retrograde gas condensate. J Petrol Sci Eng 208:109284. https:\/\/doi.org\/10.1016\/j.petrol.2021.109284","journal-title":"J Petrol Sci Eng"},{"key":"9201_CR60","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"9201_CR61","doi-asserted-by":"crossref","unstructured":"Rokach L, Maimon O (2007) Data mining with decision trees: theory and applications. World Scientific","DOI":"10.1142\/6604"},{"issue":"1","key":"9201_CR62","doi-asserted-by":"publisher","first-page":"04019036","DOI":"10.1061\/(ASCE)IS.1943-555X.0000512","volume":"26","author":"SM Piryonesi","year":"2020","unstructured":"Piryonesi SM, El-Diraby TE (2020) Data analytics in asset management: cost-effective prediction of the pavement condition index. J Infrastruct Syst 26(1):04019036. https:\/\/doi.org\/10.1061\/(ASCE)IS.1943-555X.0000512","journal-title":"J Infrastruct Syst"},{"key":"9201_CR63","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The elements of statistical learning: data mining, inference, and prediction","author":"T Hastie","year":"2009","unstructured":"Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, Berlin","edition":"2"},{"key":"9201_CR64","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et al. 2017 LightGBM: A highly efficient gradient boosting decision tree. In: Advances in neural information processing systems. Long Beach, CA, USA. p. 3146\u20133154"},{"issue":"1","key":"9201_CR65","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"9201_CR66","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.energy.2019.06.142","volume":"183","author":"B Chen","year":"2019","unstructured":"Chen B, Pawar RJ (2019) Characterization of CO2 storage and enhanced oil recovery in residual oil zones. Energy 183:291\u2013304. https:\/\/doi.org\/10.1016\/j.energy.2019.06.142","journal-title":"Energy"},{"issue":"6","key":"9201_CR67","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1021\/ci034160g","volume":"43","author":"V Svetnik","year":"2003","unstructured":"Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43(6):1947\u20131958. https:\/\/doi.org\/10.1021\/ci034160g","journal-title":"J Chem Inf Comput Sci"},{"issue":"1","key":"9201_CR68","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63(1):3\u201342. https:\/\/doi.org\/10.1007\/s10994-006-6226-1","journal-title":"Mach Learn"},{"key":"9201_CR69","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.cpc.2018.02.018","volume":"228","author":"PC Bhat","year":"2018","unstructured":"Bhat PC, Prosper HB, Sekmen S, Stewart C (2018) Optimizing event selection with the random grid search. Comput Phys Commun 228:245\u2013257. https:\/\/doi.org\/10.1016\/j.cpc.2018.02.018","journal-title":"Comput Phys Commun"},{"key":"9201_CR70","first-page":"3245","volume-title":"Encyclopedia of optimization","author":"HE Romeijn","year":"2009","unstructured":"Romeijn HE (2009) Random search methods random search methods. In: Floudas CA, Pardalos PM (eds) Encyclopedia of optimization. Springer, Boston, MA, pp 3245\u20133251"},{"key":"9201_CR71","doi-asserted-by":"crossref","unstructured":"Al-Mahroos FM, Tjoa GH (1987) Analysis and phase behavior of khuff gas system in bahrain field. Middle East Oil Show","DOI":"10.2118\/15766-MS"},{"key":"9201_CR72","first-page":"248","volume-title":"New strategic method to tune equation-of-state to match experimental data for compositional simulation","author":"AA Al-Meshari","year":"2004","unstructured":"Al-Meshari AA (2004) New strategic method to tune equation-of-state to match experimental data for compositional simulation. Texas A&M University, Ann Arbor, p 248"},{"key":"9201_CR73","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jngse.2015.03.005","volume":"24","author":"F Ameli","year":"2015","unstructured":"Ameli F, Dabir B (2015) Application of a smart mesh generation technique in gas condensate reservoirs: auto-tune PVT package for property estimation. J Natural Gas Sci Eng 24:1\u201317. https:\/\/doi.org\/10.1016\/j.jngse.2015.03.005","journal-title":"J Natural Gas Sci Eng"},{"issue":"2","key":"9201_CR74","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1016\/j.fluid.2007.07.075","volume":"260","author":"M Bonyadi","year":"2007","unstructured":"Bonyadi M, Esmaeilzadeh F (2007) Prediction of gas condensate properties by Esmaeilzadeh\u2013Roshanfekr equation of state. Fluid Phase Equilib 260(2):326\u2013334. https:\/\/doi.org\/10.1016\/j.fluid.2007.07.075","journal-title":"Fluid Phase Equilib"},{"issue":"10","key":"9201_CR75","doi-asserted-by":"publisher","first-page":"1870","DOI":"10.2118\/10512-pa","volume":"37","author":"KH Coats","year":"1985","unstructured":"Coats KH (1985) Simulation of gas condensate reservoir performance. J Petrol Technol 37(10):1870\u20131886. https:\/\/doi.org\/10.2118\/10512-pa","journal-title":"J Petrol Technol"},{"issue":"03","key":"9201_CR76","doi-asserted-by":"publisher","first-page":"277","DOI":"10.2118\/11197-pa","volume":"1","author":"KH Coats","year":"1986","unstructured":"Coats KH, Smart GT (1986) Application of a regression-based EOS PVT program to laboratory data. SPE Reserv Eng 1(03):277\u2013299. https:\/\/doi.org\/10.2118\/11197-pa","journal-title":"SPE Reserv Eng"},{"issue":"3","key":"9201_CR77","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1021\/ef000263e","volume":"15","author":"J-L Daridon","year":"2001","unstructured":"Daridon J-L, Pauly J, Coutinho JAP, Montel F (2001) Solid\u2212liquid\u2212vapor phase boundary of a north sea waxy crude: measurement and modeling. Energy Fuels 15(3):730\u2013735. https:\/\/doi.org\/10.1021\/ef000263e","journal-title":"Energy Fuels"},{"issue":"5","key":"9201_CR78","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1002\/cjce.5450560515","volume":"56","author":"A Firoozabadi","year":"1978","unstructured":"Firoozabadi A, Hekim Y, Katz DL (1978) Reservoir depletion calculations for gas condensates using extended analyses in the peng-robinson equation of state. Canad J Chem Eng 56(5):610\u2013615. https:\/\/doi.org\/10.1002\/cjce.5450560515","journal-title":"Canad J Chem Eng"},{"issue":"1","key":"9201_CR79","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2118\/219-g","volume":"5","author":"AE Hoffman","year":"1953","unstructured":"Hoffman AE, Crump JS, Hocott CR (1953) Equilibrium constants for a gas-condensate system. J Petrol Technol 5(1):1\u201310. https:\/\/doi.org\/10.2118\/219-g","journal-title":"J Petrol Technol"},{"key":"9201_CR80","volume-title":"Natural gas production engineering","author":"M Kelkar","year":"2008","unstructured":"Kelkar M (2008) Natural gas production engineering. PennWell Books, OK"},{"issue":"8","key":"9201_CR81","doi-asserted-by":"publisher","first-page":"981","DOI":"10.2118\/12278-pa","volume":"39","author":"D Kenyon","year":"1987","unstructured":"Kenyon D (1987) Third SPE comparative solution project: gas cycling of retrograde condensate reservoirs. J Petrol Technol 39(8):981\u2013997. https:\/\/doi.org\/10.2118\/12278-pa","journal-title":"J Petrol Technol"},{"key":"9201_CR82","unstructured":"Kurata F, Katz DLV (1942) Critical properties of volatile hydrocarbon mixtures. University of Michigan"},{"key":"9201_CR83","unstructured":"Pedersen KS, Thomassen P, Fredenslund A (1988) Proceedings of the Presentation at the 1988. AIChE Spring National Meeting. New Orleans, LA, USA"},{"issue":"9","key":"9201_CR84","doi-asserted-by":"publisher","first-page":"261","DOI":"10.2118\/950261-G","volume":"2","author":"H Reamer","year":"1950","unstructured":"Reamer H, Sage B (1950) Volumetric behavior of oil and gas from a Louisiana field I. J Petrol Technol 2(9):261\u2013268","journal-title":"J Petrol Technol"},{"issue":"1","key":"9201_CR85","doi-asserted-by":"publisher","first-page":"156","DOI":"10.2118\/947156-g","volume":"170","author":"BH Sage","year":"1947","unstructured":"Sage BH, Olds RH (1947) Volumetric behavior of oil and gas from several san joaquin valley fields. Trans AIME 170(1):156\u2013173. https:\/\/doi.org\/10.2118\/947156-g","journal-title":"Trans AIME"},{"issue":"1","key":"9201_CR86","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/S0378-3812(97)00178-7","volume":"139","author":"M Sportisse","year":"1997","unstructured":"Sportisse M, Barreau A, Ungerer P (1997) Modeling of gas condensates properties using continuous distribution functions for the characterisation of the heavy fraction. Fluid Phase Equilib 139(1):255\u2013276. https:\/\/doi.org\/10.1016\/S0378-3812(97)00178-7","journal-title":"Fluid Phase Equilib"},{"key":"9201_CR87","doi-asserted-by":"crossref","unstructured":"Vogel JL, Yarborough L (1980) The effect of nitrogen on the phase behavior and physical properties of reservoir fluids. In: SPE\/DOE Enhanced oil recovery symposium","DOI":"10.2118\/8815-MS"},{"issue":"1","key":"9201_CR88","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/S0378-3812(96)03163-9","volume":"128","author":"T Yang","year":"1997","unstructured":"Yang T, Chen WD, Guo TM (1997) Phase behavior of a near-critical reservoir fluid mixture. Fluid Phase Equilib 128(1):183\u2013197. https:\/\/doi.org\/10.1016\/S0378-3812(96)03163-9","journal-title":"Fluid Phase Equilib"},{"key":"9201_CR89","unstructured":"McKinney W, Team PD (2021) Pandas-Powerful python data analysis toolkit. Pandas\u2014Powerful Python Data Anal Toolkit. Release 1.3.5:https:\/\/pandas.pydata.org\/."},{"key":"9201_CR90","volume-title":"Robust regression and outlier detection","author":"PJ Rousseeuw","year":"2005","unstructured":"Rousseeuw PJ, Leroy AM (2005) Robust regression and outlier detection. Wiley, Hoboken"},{"key":"9201_CR91","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/S0169-7161(05)80137-3","volume":"9","author":"CR Goodall","year":"1993","unstructured":"Goodall CR (1993) 13 Computation using the QR decomposition. Handbook Statist 9:467\u2013508","journal-title":"Handbook Statist"},{"issue":"5","key":"9201_CR92","first-page":"694","volume":"26","author":"P Gramatica","year":"2007","unstructured":"Gramatica P (2007) Principles of QSAR models validation: internal and external. Mol Inf 26(5):694\u2013701","journal-title":"Mol Inf"},{"key":"9201_CR93","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1177\/026119290503300209","volume":"33","author":"TI Netzeva","year":"2005","unstructured":"Netzeva TI, Worth AP, Aldenberg T, Benigni R, Cronin MT, Gramatica P et al (2005) Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships: the report and recommendations of ECVAM Workshop 521,2. Altern Lab Anim 33:155\u2013173","journal-title":"Altern Lab Anim"},{"issue":"2","key":"9201_CR94","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1007\/s10910-009-9609-2","volume":"47","author":"M Meloun","year":"2010","unstructured":"Meloun M, Bordovsk\u00e1 S, Kupka K (2010) Outliers detection in the statistical accuracy test of ap K a prediction. J Math Chem 47(2):891\u2013909","journal-title":"J Math Chem"},{"issue":"2","key":"9201_CR95","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1021\/ef0498465","volume":"19","author":"K Nasrifar","year":"2005","unstructured":"Nasrifar K, Bolland O, Moshfeghian M (2005) Predicting natural gas dew points from 15 equations of state. Energy Fuels 19(2):561\u2013572. https:\/\/doi.org\/10.1021\/ef0498465","journal-title":"Energy Fuels"},{"key":"9201_CR96","doi-asserted-by":"publisher","DOI":"10.1201\/9781420018257","volume-title":"Phase behavior of petroleum reservoir fluids","author":"KS Pedersen","year":"2006","unstructured":"Pedersen KS, Christensen PL, Shaikh JA, Christensen PL (2006) Phase behavior of petroleum reservoir fluids. CRC Press, Cambridge"},{"issue":"4","key":"9201_CR97","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1016\/S0009-2509(01)00404-3","volume":"57","author":"D Ronze","year":"2002","unstructured":"Ronze D, Fongarland P, Pitault I, Forissier M (2002) Hydrogen solubility in straight run gasoil. Chem Eng Sci 57(4):547\u2013553. https:\/\/doi.org\/10.1016\/S0009-2509(01)00404-3","journal-title":"Chem Eng Sci"},{"issue":"3","key":"9201_CR98","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/0378-3812(91)80036-U","volume":"63","author":"A Danesh","year":"1991","unstructured":"Danesh A, Xu DH, Todd AC (1991) Comparative study of cubic equations of state for predicting phase behaviour and volumetric properties of injection gas-reservoir oil systems. Fluid Phase Equilib 63(3):259\u2013278. https:\/\/doi.org\/10.1016\/0378-3812(91)80036-U","journal-title":"Fluid Phase Equilib"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09201-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09201-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09201-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T18:20:25Z","timestamp":1704997225000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09201-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,18]]},"references-count":98,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["9201"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09201-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,18]]},"assertion":[{"value":"25 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}