{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T09:14:17Z","timestamp":1773998057836,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2017,1,18]],"date-time":"2017-01-18T00:00:00Z","timestamp":1484697600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2018,11]]},"DOI":"10.1007\/s00521-017-2850-x","type":"journal-article","created":{"date-parts":[[2017,1,18]],"date-time":"2017-01-18T06:56:33Z","timestamp":1484722593000},"page":"2673-2683","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":106,"title":["New insights into the prediction of heterogeneous carbonate reservoir permeability from well logs using artificial intelligence network"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7209-3715","authenticated-orcid":false,"given":"Salaheldin","family":"Elkatatny","sequence":"first","affiliation":[]},{"given":"Mohamed","family":"Mahmoud","sequence":"additional","affiliation":[]},{"given":"Zeeshan","family":"Tariq","sequence":"additional","affiliation":[]},{"given":"Abdulazeez","family":"Abdulraheem","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,1,18]]},"reference":[{"key":"2850_CR1","doi-asserted-by":"publisher","unstructured":"Gunter G, Finneran J, Hartmann D, Miller J (1997) Early determination of reservoir flow units using an integrated petrophysical method. In: Proceedings of SPE annual technical conference and exhibition, San Antonio, Texas, paper 38679. doi:\n                        10.2118\/38679-MS","DOI":"10.2118\/38679-MS"},{"key":"2850_CR2","unstructured":"Morris R, Briggs W (1967) Using log-derived values of water saturation and porosity. In: Proceedings of SPE and well-log analysts 8th annual logging symposium, Denver, Colorado, paper 1967-X"},{"key":"2850_CR3","doi-asserted-by":"publisher","unstructured":"Fleury M (2002) Resistivity in carbonates: new insights. In: Proceedings of SPE annual technical conference and exhibition, San Antonio, Texas, paper 77719. doi:\n                        10.2118\/77719-MS","DOI":"10.2118\/77719-MS"},{"key":"2850_CR4","doi-asserted-by":"publisher","first-page":"215","DOI":"10.2118\/84942-PA","volume":"6","author":"J Jennings","year":"2003","unstructured":"Jennings J, Lucia F (2003) Predicting permeability from well logs in carbonates with a link to geology for interwell permeability mapping. SPE Reserv Eval Eng 6:215\u2013225. doi:\n                        10.2118\/84942-PA","journal-title":"SPE Reserv Eval Eng"},{"key":"2850_CR5","doi-asserted-by":"publisher","unstructured":"Lacentre P, Carrica P (2003) A method to estimate permeability on uncored wells based on well logs and core data. In: Proceedings of SPE Latin American and Caribbean petroleum engineering conference, Port-of-Spain, Trinidad and Tobago, paper-81058. doi:\n                        10.2118\/81058-MS","DOI":"10.2118\/81058-MS"},{"key":"2850_CR6","doi-asserted-by":"publisher","first-page":"143","DOI":"10.2118\/84301-PA","volume":"8","author":"H Perez","year":"2005","unstructured":"Perez H, Datta-Gupta A, Mishra S (2005) The role of electrofacies, lithofacies, and hydraulic flow units in permeability predictions from well logs: a comparative analysis using classification trees. SPE Reserv Eval Eng 8:143\u2013155. doi:\n                        10.2118\/84301-PA","journal-title":"SPE Reserv Eval Eng"},{"key":"2850_CR7","unstructured":"Yarra S, Budi A, Doni H (2008) Using MRGC (multi resolution graph-based clustering) method to integrate log data analysis and core facies to define electrofacies, in the Benua field. In: Central Sumatera Basin, Indonesia, international gas union research conference, Paris, France"},{"key":"2850_CR8","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1109\/TPAMI.2008.137","volume":"31","author":"A Graves","year":"2009","unstructured":"Graves A, Liwicki M, Fernandez S, Bertolami R, Bunke H, Schmidhuber J (2009) A novel connectionist system for improved unconstrained handwriting recognition. IEEE Trans Pattern Anal Mach Intell 31:855\u2013868. doi:\n                        10.1109\/TPAMI.2008.137","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2850_CR9","doi-asserted-by":"publisher","unstructured":"Nooruddin H, Anifowose F, Abdulraheem A (2013) Applying artificial intelligence techniques to develop permeability predictive models using mercury injection capillary-pressure data. In: Proceedings of SPE Saudi Arabia section technical symposium and exhibition, Khobar, Saudi Arabia, paper 168109. doi:\n                        10.2118\/168109-MS","DOI":"10.2118\/168109-MS"},{"key":"2850_CR10","doi-asserted-by":"publisher","unstructured":"Abdulraheem A, Ahmed M, Vantala A, Parvez T (2009) Prediction of rock mechanical parameters for hydrocarbon reservoirs using different artificial intelligence techniques. In: Proceedings of SPE Saudi Arabia section technical symposium, Al-Khobar, Saudi Arabia, paper 126094. doi:\n                        10.2118\/126094-MS","DOI":"10.2118\/126094-MS"},{"key":"2850_CR11","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"G Hinton","year":"2006","unstructured":"Hinton G, Osindero S, Teh Y (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527\u20131554. doi:\n                        10.1162\/neco.2006.18.7.1527","journal-title":"Neural Comput"},{"key":"2850_CR12","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1109\/21.256541","volume":"23","author":"J Jang","year":"1993","unstructured":"Jang J (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665\u2013685. doi:\n                        10.1109\/21.256541","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"2850_CR13","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.cageo.2012.02.004","volume":"42","author":"P Tahmasebi","year":"2012","unstructured":"Tahmasebi P (2012) A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Comput Geosci 42:18\u201327. doi:\n                        10.1016\/j.cageo.2012.02.004","journal-title":"Comput Geosci"},{"key":"2850_CR14","doi-asserted-by":"publisher","first-page":"125","DOI":"10.4249\/scholarpedia.5187","volume":"2","author":"A Ben-Hur","year":"2001","unstructured":"Ben-Hur A, Horn D, Siegelmann H, Vapnik V (2001) Support vector clustering. J Mach Learn Res 2:125\u2013137. doi:\n                        10.4249\/scholarpedia.5187","journal-title":"J Mach Learn Res"},{"key":"2850_CR15","unstructured":"Press W, Teukolsky S, Vetterling W, Flannery B (2007) Numerical recipes: the art of scientific computing, 3rd ed. Cambridge University Press, New York. ISBN: 13:978-0521880688"},{"key":"2850_CR16","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.petrol.2005.05.005","volume":"49","author":"J Lime","year":"2005","unstructured":"Lime J (2005) Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Kore. J Pet Sci Eng 49:182\u2013192. doi:\n                        10.1016\/j.petrol.2005.05.005","journal-title":"J Pet Sci Eng"},{"key":"2850_CR17","doi-asserted-by":"publisher","first-page":"266","DOI":"10.2118\/87038-PA","volume":"9","author":"E Shokir","year":"2006","unstructured":"Shokir E (2006) A novel model for permeability prediction in uncored wells. SPE Reserv Eval Eng 9:266\u2013273. doi:\n                        10.2118\/87038-PA","journal-title":"SPE Reserv Eval Eng"},{"key":"2850_CR18","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1007\/s00521-010-0501-6","volume":"21","author":"N Al-Bulushi","year":"2010","unstructured":"Al-Bulushi N, King P, Blunt M, Kraaijveld M (2010) Artificial neural networks workflow and its application in the petroleum industry. Neural Comput Appl 21:409\u2013421. doi:\n                        10.1007\/s00521-010-0501-6","journal-title":"Neural Comput Appl"},{"key":"2850_CR19","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.compind.2010.10.008","volume":"62","author":"S Olatunji","year":"2010","unstructured":"Olatunji S, Selamat A, Abdulraheem A (2010) Modeling the permeability of carbonate reservoir using type-2 fuzzy logic systems. Comput Ind 62:147\u2013163. doi:\n                        10.1016\/j.compind.2010.10.008","journal-title":"Comput Ind"},{"key":"2850_CR20","first-page":"41","volume":"2","author":"R Gholami","year":"2011","unstructured":"Gholami R, Moradzadeh A (2011) Support vector regression for prediction of gas reservoirs permeability. J Min Environ 2:41\u201352","journal-title":"J Min Environ"},{"key":"2850_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2012\/670723","volume":"2012","author":"R Gholami","year":"2012","unstructured":"Gholami R, Shahraki A, Paghaleh M (2012) Prediction of hydrocarbon reservoirs permeability using support vector machine. Math Probl Eng 2012:1\u201318. doi:\n                        10.1155\/2012\/670723","journal-title":"Math Probl Eng"},{"key":"2850_CR22","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1109\/72.97934","volume":"2","author":"F Donald","year":"1991","unstructured":"Donald F (1991) A general regression neural network. IEEE Trans Neural Netw 2:568\u2013576. doi:\n                        10.1109\/72.97934","journal-title":"IEEE Trans Neural Netw"},{"key":"2850_CR23","doi-asserted-by":"publisher","first-page":"5308","DOI":"10.5897\/IJPS12.420","volume":"7","author":"A Mohamed","year":"2012","unstructured":"Mohamed A (2012) Estimation of permeability using artificial neural networks and regression analysis in an iran oil field. Int J Phys Sci 7:5308\u20135313. doi:\n                        10.5897\/IJPS12.420","journal-title":"Int J Phys Sci"},{"key":"2850_CR24","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.5829\/idosi.mejsr.2013.13.9.927","volume":"13","author":"A Hatampour","year":"2013","unstructured":"Hatampour A, Ramzi R, Sedaghat M (2013) Improving performance of a neural network model by artificial ant colon optimization for predicting permeability of petroleum reservoir rocks. Middle East J Sci Res 13:1217\u20131223. doi:\n                        10.5829\/idosi.mejsr.2013.13.9.927","journal-title":"Middle East J Sci Res"},{"key":"2850_CR25","unstructured":"Klinkenberg L (1941) The permeability of porous media to liquids and gases. In: American Petroleum Institute, drilling and productions practices, New York, New York, paper API-41-200"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-017-2850-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-2850-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-2850-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,10,29]],"date-time":"2018-10-29T11:58:49Z","timestamp":1540814329000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-017-2850-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,1,18]]},"references-count":25,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2018,11]]}},"alternative-id":["2850"],"URL":"https:\/\/doi.org\/10.1007\/s00521-017-2850-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,1,18]]}}}