{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T20:00:54Z","timestamp":1760299254063,"version":"3.37.3"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2015,11,24]],"date-time":"2015-11-24T00:00:00Z","timestamp":1448323200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004919","name":"King Abdulaziz City for Science and Technology (SA)","doi-asserted-by":"publisher","award":["GSP-18-101"],"award-info":[{"award-number":["GSP-18-101"]}],"id":[{"id":"10.13039\/501100004919","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":[[2017,4]]},"DOI":"10.1007\/s00521-015-2088-4","type":"journal-article","created":{"date-parts":[[2015,11,24]],"date-time":"2015-11-24T01:14:33Z","timestamp":1448327673000},"page":"635-649","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Prediction of non-hydrocarbon gas components in separator by using Hybrid Computational Intelligence models"],"prefix":"10.1007","volume":"28","author":[{"given":"Tarek","family":"Helmy","sequence":"first","affiliation":[]},{"given":"Muhammad Imtiaz","family":"Hossain","sequence":"additional","affiliation":[]},{"given":"Abdulazeez","family":"Adbulraheem","sequence":"additional","affiliation":[]},{"given":"S. M.","family":"Rahman","sequence":"additional","affiliation":[]},{"given":"Md. Rafiul","family":"Hassan","sequence":"additional","affiliation":[]},{"given":"Amar","family":"Khoukhi","sequence":"additional","affiliation":[]},{"given":"M.","family":"Elshafei","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2015,11,24]]},"reference":[{"issue":"3\u20134","key":"2088_CR1","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.enggeo.2010.05.005","volume":"114","author":"A Al-Anazi","year":"2010","unstructured":"Al-Anazi A, Gates ID (2010) A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs. Eng Geol 114(3\u20134):267\u2013277","journal-title":"Eng Geol"},{"key":"2088_CR2","doi-asserted-by":"crossref","unstructured":"Al-Anazi A, Gates ID, Azaiez J (2009) Innovative data-driven permeability prediction in a heterogeneous reservoir. In: SPE EUROPEC\/EAGE annual conference and exhibition, Amsterdam, The Netherlands","DOI":"10.2118\/121159-MS"},{"issue":"1\u20132","key":"2088_CR3","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.petrol.2006.05.001","volume":"53","author":"FA Al-Farhan","year":"2006","unstructured":"Al-Farhan FA, Ayala LF (2006) Optimization of surface condensate production from natural gases using artificial intelligence. J Petrol Sci Eng 53(1\u20132):135\u2013147","journal-title":"J Petrol Sci Eng"},{"key":"2088_CR4","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1016\/S0098-3004(00)00025-X","volume":"26","author":"M Ali","year":"2000","unstructured":"Ali M, Chawathe A (2000) Using artificial intelligence to predict permeability from petrographic data. Comput Geosci 26:915\u2013925","journal-title":"Comput Geosci"},{"key":"2088_CR5","doi-asserted-by":"crossref","unstructured":"Almeida MB, Braga AP, Braga JP (2000) SVM-KM: speeding SVMs learning with a priori cluster selection and k-means. In: Proceedings of the 6th Brazilian symposium on neural networks, pp 162\u2013167","DOI":"10.1109\/SBRN.2000.889732"},{"issue":"3","key":"2088_CR6","first-page":"231","volume":"1","author":"S Ameri","year":"1994","unstructured":"Ameri S, Aminian K, Mohaghegh S (1994) Predicting the production performance of gas reservoirs. Sci Iran 1(3):231\u2013240","journal-title":"Sci Iran"},{"key":"2088_CR7","doi-asserted-by":"crossref","unstructured":"Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Haussler D (ed) Proceedings of the 5th annual ACM workshop on computational learning theory, pp 144\u2013152","DOI":"10.1145\/130385.130401"},{"key":"2088_CR8","doi-asserted-by":"crossref","unstructured":"Briones MF, Corpoven SA, Rojas GA, Martinez ER (1994) Application of neural network in the prediction of reservoir hydrocarbon mixture composition from production data. In: SPE annual technical conference and exhibition, New Orleans, Louisiana","DOI":"10.2118\/28598-MS"},{"issue":"1","key":"2088_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.advwatres.2005.04.015","volume":"29","author":"F Chang","year":"2006","unstructured":"Chang F, Chang Y (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Resour 29(1):1\u201310","journal-title":"Adv Water Resour"},{"key":"2088_CR10","doi-asserted-by":"crossref","unstructured":"Chen J, Li Z, Zhao D (2009) Prediction of hydrocarbon reservoir parameter using a GA-RBF neural network. In: Computational intelligence and intelligent systems, 4th international symposium, ISICA 2009, Huangshi, China, pp 379\u2013386","DOI":"10.1007\/978-3-642-04962-0_43"},{"key":"2088_CR11","doi-asserted-by":"publisher","first-page":"289239","DOI":"10.1155\/2014\/289239","volume":"2014","author":"CG Monyei","year":"2014","unstructured":"Monyei CG, Adewumi AO, Obolo MO (2014) Oil well characterization and artificial gas lift optimization using neural networks combined with genetic algorithm. Discrete Dyn Nat Soc 2014:289239. doi: 10.1155\/2014\/289239","journal-title":"Discrete Dyn Nat Soc"},{"key":"2088_CR12","doi-asserted-by":"crossref","unstructured":"Elshafei M, Khoukhi A, Abdulraheem A (2010) Neural network aided design of oil production units. In: 2010 10th International conference on information sciences signal processing and their applications (ISSPA). IEEE, pp 638\u2013641","DOI":"10.1109\/ISSPA.2010.5605420"},{"key":"2088_CR13","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, Perth, Australia","DOI":"10.2118\/49961-MS"},{"issue":"2","key":"2088_CR14","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1021\/ef970135z","volume":"12","author":"AM Elsharkawy","year":"1998","unstructured":"Elsharkawy AM, Foda SG (1998) EOS simulation and GRNN modeling of the constant volume depletion behavior of gas condensate reservoirs. Energy Fuels 12(2):353\u2013364","journal-title":"Energy Fuels"},{"key":"2088_CR15","first-page":"413","volume":"5","author":"FA Anifowose","year":"2013","unstructured":"Anifowose FA, Labadin J, Abdulraheem A (2013) Prediction of petroleum reservoir properties using different versions of adaptive neuro-fuzzy inference system hybrid models. Int J Comput Inf Syst Ind Manag Appl 5:413\u2013426","journal-title":"Int J Comput Inf Syst Ind Manag Appl"},{"issue":"3","key":"2088_CR16","doi-asserted-by":"crossref","first-page":"6326","DOI":"10.1016\/j.eswa.2008.08.012","volume":"36","author":"S Fei","year":"2009","unstructured":"Fei S, Liu C, Miao Y (2009) Support vector machine with genetic algorithm for forecasting of key-gas ratios in oil-immersed transformer. Expert Syst Appl 36(3):6326\u20136331","journal-title":"Expert Syst Appl"},{"issue":"67","key":"2088_CR17","first-page":"817","volume":"3","author":"JJ Finol","year":"2001","unstructured":"Finol JJ, Guo YK, Jing XDD (2001) Permeability prediction in shaly formations: the fuzzy modeling approach. Geophysics 3(67):817\u2013829","journal-title":"Geophysics"},{"key":"2088_CR18","unstructured":"Ghouti L, Bukhitan S (2010). Hybrid soft computing for PVT properties prediction. In: European symposium on artificial neural networks, computational intelligence and machine learning (ESANN), Belgium"},{"key":"2088_CR19","doi-asserted-by":"crossref","unstructured":"Goda HM, Fattah KA, Shokir EM, Sayyouh MH (2003) Prediction of the PVT data using neural network computing theory. In: 27th Annual SPE international technical conference and exhibition, Abuja, Nigeria","DOI":"10.2118\/85650-MS"},{"key":"2088_CR20","isbn-type":"print","volume-title":"Genetic algorithms in search, optimization, and machine learning","author":"DE Goldberg","year":"1989","unstructured":"Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading. ISBN 978-0201157673","ISBN":"https:\/\/id.crossref.org\/isbn\/9780201157673"},{"key":"2088_CR21","doi-asserted-by":"crossref","unstructured":"Hallman JH, Cook I, Muqeem MA, Jarrett CM, Shammari HA (2007) Fluid customization and equipment optimization enable safe and successful underbalanced drilling of high-H2S horizontal wells in Saudi Arabia. In: IADC\/SPE managed pressure drilling and underbalanced operations conference and exhibition, Galveston, Texas, USA","DOI":"10.2118\/108332-MS"},{"key":"2088_CR22","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.cageo.2013.11.007","volume":"64","author":"H Nooruddin","year":"2014","unstructured":"Nooruddin H, Anifowose F, Abdulazeez A (2014) Using soft computing techniques to predict corrected air permeability using Thomeer parameters, air porosity and grain density. Comput Geosci 64:72\u201380","journal-title":"Comput Geosci"},{"issue":"4","key":"2088_CR23","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1142\/S1469026810002902","volume":"9","author":"T Helmy","year":"2010","unstructured":"Helmy T, Fatai A (2010) Hybrid computational intelligence models for porosity and permeability prediction of petroleum reservoirs. Int J Comput Intell Appl 9(4):313\u2013337","journal-title":"Int J Comput Intell Appl"},{"key":"2088_CR24","doi-asserted-by":"crossref","first-page":"5353","DOI":"10.1016\/j.eswa.2010.01.021","volume":"37","author":"T Helmy","year":"2010","unstructured":"Helmy T, Fatai A, Faisal KA (2010) Hybrid computational models for the characterization of oil and gas reservoirs. Expert Syst Appl 37:5353\u20135363","journal-title":"Expert Syst Appl"},{"key":"2088_CR25","unstructured":"Hossain MI, Helmy T, Hassan MR, Adbulraheem A, Khoukhi A, Elshafei M (2012) Non-hydrocarbons gas components prediction in multistage separator using neural networks. In: Global conference on power control and optimization PCO, Dubai"},{"key":"2088_CR26","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s10596-008-9095-9","volume":"13","author":"N Hurtado","year":"2009","unstructured":"Hurtado N, Aldana M, Torres J (2009) Comparison between neuro-fuzzy and fractal models for permeability prediction. Comput Geosci 13:181\u2013186","journal-title":"Comput Geosci"},{"key":"2088_CR27","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1088\/1742-2132\/3\/4\/007","volume":"3","author":"K Ilkhchi","year":"2006","unstructured":"Ilkhchi K (2006) A fuzzy logic approach for the estimation of permeability and rock types from conventional well log data: an example from the Kangan reservoir in Iran Offshore Gas Field. Iran J Geophys Eng 3:356\u2013369","journal-title":"Iran J Geophys Eng"},{"issue":"3","key":"2088_CR28","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1109\/21.256541","volume":"23","author":"JSR Jang","year":"1993","unstructured":"Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665\u2013685","journal-title":"IEEE Trans Syst Man Cybern"},{"issue":"1\u20134","key":"2088_CR29","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0920-4105(02)00153-5","volume":"34","author":"JN Jaubert","year":"2002","unstructured":"Jaubert JN, Avaullee L, Souvay JF (2002) A crude oil data bank containing more than 5000 PVT and gas injection data. J Petrol Sci Eng 34(1\u20134):65\u2013107","journal-title":"J Petrol Sci Eng"},{"key":"2088_CR30","doi-asserted-by":"crossref","first-page":"3063","DOI":"10.1021\/ef9001346","volume":"23","author":"RS Jupudi","year":"2009","unstructured":"Jupudi RS, Zamansky V, Fletcher TH (2009) Prediction of light gas composition in coal devolatilization. Am Chem Soc Energy Fuels 23:3063\u20133067","journal-title":"Am Chem Soc Energy Fuels"},{"issue":"3","key":"2088_CR31","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1142\/S1469026811003100","volume":"10","author":"A Khoukhi","year":"2011","unstructured":"Khoukhi A, Oloso M, Abdulraheem A, El-Shafei M, Al-Majed A (2011) Support vector machines and functional networks for viscosity and gas\/oil ratio curves prediction. Int J Comput Intell Appl 10(3):269\u2013293","journal-title":"Int J Comput Intell Appl"},{"issue":"8","key":"2088_CR32","first-page":"1866","volume":"6","author":"A Lashin","year":"2012","unstructured":"Lashin A, El-Din SS (2012) Reservoir parameters determination using artificial neural networks: Ras Fanar field, Gulf of Suez, Egypt. Arab J Geosci 6(8):1866\u20137511","journal-title":"Egypt. Arab J Geosci"},{"key":"2088_CR33","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.petrol.2013.05.008","volume":"108","author":"M Mahmudi","year":"2013","unstructured":"Mahmudi M, Sadeghi MT (2013) The optimization of continuous gas lift process using an integrated compositional model. J Petrol Sci Eng 108:321\u2013327","journal-title":"J Petrol Sci Eng"},{"issue":"1","key":"2088_CR34","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1590\/S0104-66322009000100019","volume":"26","author":"AR Moghadassi","year":"2009","unstructured":"Moghadassi AR, Parvizian F, Hosseini SM, Fazalali AR (2009) A new approach for estimation of PVT properties of pure gases based on artificial neural network model. Braz J Chem Eng 26(1):199\u2013206","journal-title":"Braz J Chem Eng"},{"key":"2088_CR35","doi-asserted-by":"crossref","unstructured":"Mohaghegh S, Balan B, Ameri S, McVey DS (1996) A hybrid neuro-genetic approach to hydraulic fracture treatment design and optimization. In: SPE annual technical conference and exhibition, Denver, Colorado, USA","DOI":"10.2118\/36602-MS"},{"key":"2088_CR36","doi-asserted-by":"crossref","unstructured":"Mohaghegh S, Platon V, Ameri S (1998) Candidate selection for stimulation of gas storage wells using available data with neural networks and genetic algorithms. In: SPE eastern regional meeting, Pittsburgh, Pennsylvania","DOI":"10.2118\/51080-MS"},{"key":"2088_CR37","doi-asserted-by":"crossref","unstructured":"Nagi J, Kiong TS, Ahmed SK, Nagi F (2009) Prediction of PVT properties in crude oil systems using support vector machines. In: Proceedings of ICEE 2009 3rd international conference on energy and environment, Malacca, Malaysia","DOI":"10.1109\/ICEENVIRON.2009.5398681"},{"key":"2088_CR38","first-page":"3","volume-title":"Developments in petroleum science series","author":"M Nikravesh","year":"2003","unstructured":"Nikravesh M, Aminzadeh F (2003) Soft computing for intelligent reservoir characterization and modeling. In: Soft Computing and Intelligent Data Analysis, Nikravesh M, Aminzadeh F, Zadeh LA (eds) Developments in petroleum science series, vol 51. Elsevier, Amsterdam, pp 3\u201332"},{"key":"2088_CR39","doi-asserted-by":"crossref","unstructured":"Oloso MA, Khoukhi A, Abdulraheem A, Elshafei M (2009a) A genetic-optimized artificial neural networks for predicting viscosity and gas\/oil ratio curves. In: SPE\/EAGE reservoir characterization and simulation conference and exhibition, Abu-Dhabi, UAE","DOI":"10.2118\/125360-MS"},{"key":"2088_CR40","doi-asserted-by":"crossref","unstructured":"Oloso MA, Khoukhi A, Abdulraheem A, Elshafei M (2009b) Prediction of crude oil viscosity and gas\/oil ratio curves using recent advances to neural networks. In: SPE\/EAGE reservoir characterization and simulation conference, Abu-Dhabi, UAE","DOI":"10.2118\/125360-MS"},{"key":"2088_CR41","doi-asserted-by":"crossref","unstructured":"Osman EA, Al-Marhoun MA (2005) Artificial neural networks models for predicting PVT properties of oil field brines. In: 14th SPE middle east oil and gas show and conference, Bahrain","DOI":"10.2118\/93765-MS"},{"key":"2088_CR42","doi-asserted-by":"crossref","unstructured":"Osman EA, Abdel-Wahhab OA, Al-Marhoun MA (2001) Prediction of oil PVT properties using neural networks. In: SPE middle east oil show. Society of Petroleum Engineers, Manama, Bahrain","DOI":"10.2118\/68233-MS"},{"key":"2088_CR43","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.petrol.2007.03.007","volume":"59","author":"M Saemi","year":"2007","unstructured":"Saemi M, Ahmadi M, Varjani AY (2007) Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. J Petrol Sci Eng 59:97\u2013105","journal-title":"J Petrol Sci Eng"},{"issue":"2","key":"2088_CR44","first-page":"121","volume":"29","author":"Q Sun","year":"2005","unstructured":"Sun Q, Li J, Zhang C, Liu X (2005) High watercut reservoir permeability prediction by flow unit, ANFIS and multi-statistics. Well Logging Technol 29(2):121\u2013124","journal-title":"Well Logging Technol"},{"key":"2088_CR45","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1088\/0957-0233\/2\/5\/008","volume":"2","author":"H Sundgren","year":"1991","unstructured":"Sundgren H, Winquist F, Locker I, Lundstrom I (1991) Artificial neural networks and gas sensor arrays: quantification of individual components in a gas mixture. Meas Sci Technol 2:464\u2013469","journal-title":"Meas Sci Technol"},{"key":"2088_CR46","unstructured":"Tran QA, Zhang QL, Li X (2003) Reduce the number of support vectors by using clustering techniques. In: International conference on machine learning and cybernetics, vol 2. IEEE, pp 1245\u20131248"},{"key":"2088_CR47","doi-asserted-by":"crossref","unstructured":"Varotsis N, Gaganis V, Nighswander J, Guieze P (1999) A novel non-iterative method for the prediction of the PVT behavior of reservoir fluids. In: SPE annual technical conference and exhibition, Houston, Texas, USA","DOI":"10.2118\/56745-MS"},{"issue":"7","key":"2088_CR48","doi-asserted-by":"crossref","first-page":"74","DOI":"10.2118\/69071-JPT","volume":"53","author":"P Wang","year":"2001","unstructured":"Wang P, Pope G (2001) Proper use of equations of state for compositional reservoir simulation. J Petrol Technol 53(7):74\u201381","journal-title":"J Petrol Technol"},{"key":"2088_CR49","first-page":"3","volume-title":"Soft computing for reservoir characterisation and modeling, studies in fuzziness and soft computing","author":"PM Wong","year":"2002","unstructured":"Wong PM, Aminzadeh F, Nikravesh M (2002) Intelligent reservoir characterization. In: Wong PM, Aminzadeh F, Nikravesh M (eds) Soft computing for reservoir characterisation and modeling, studies in fuzziness and soft computing. Springer, Berlin, pp 3\u201312"},{"issue":"3","key":"2088_CR50","first-page":"631","volume":"33","author":"L Xiao-mei","year":"2009","unstructured":"Xiao-mei L, Qin-hua S, Jian-xin L, Wei-fang L (2009) Prediction of fracture porosity of carbonate reservoir with seismic attributes, multi-analysis and ANFIS. Well Logging Technol 33(3):631\u2013684","journal-title":"Well Logging Technol"},{"key":"2088_CR51","doi-asserted-by":"crossref","unstructured":"Xie D, Wilkinson D, Yu T, Ramon S (2005) Permeability estimation using a hybrid genetic programming and fuzzy\/neural inference approach. In: SPE annual technical conference and exhibition, Dallas, Texas, USA","DOI":"10.2118\/95167-MS"},{"issue":"5\u20136","key":"2088_CR52","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1080\/713827180","volume":"17","author":"S Zhang","year":"2003","unstructured":"Zhang S, Zhang C, Yang Q (2003) Data preparation for data mining. Appl Artif Intell 17(5\u20136):375\u2013381","journal-title":"Appl Artif Intell"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-2088-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-015-2088-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-2088-4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-2088-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T17:05:22Z","timestamp":1567357522000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-015-2088-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,11,24]]},"references-count":52,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2017,4]]}},"alternative-id":["2088"],"URL":"https:\/\/doi.org\/10.1007\/s00521-015-2088-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2015,11,24]]}}}