{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T17:12:17Z","timestamp":1768583537172,"version":"3.49.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2018,1,13]],"date-time":"2018-01-13T00:00:00Z","timestamp":1515801600000},"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 &amp; Applic"],"published-print":{"date-parts":[[2019,8]]},"DOI":"10.1007\/s00521-018-3344-1","type":"journal-article","created":{"date-parts":[[2018,1,13]],"date-time":"2018-01-13T11:51:11Z","timestamp":1515844271000},"page":"4123-4135","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["An integrated approach for estimating static Young\u2019s modulus using artificial intelligence tools"],"prefix":"10.1007","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7209-3715","authenticated-orcid":false,"given":"Salaheldin","family":"Elkatatny","sequence":"first","affiliation":[]},{"given":"Zeeshan","family":"Tariq","sequence":"additional","affiliation":[]},{"given":"Mohamed","family":"Mahmoud","sequence":"additional","affiliation":[]},{"given":"Abdulazeez","family":"Abdulraheem","sequence":"additional","affiliation":[]},{"given":"Ibrahim","family":"Mohamed","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,1,13]]},"reference":[{"key":"3344_CR1","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.petrol.2006.01.003","volume":"51","author":"C Chang","year":"2006","unstructured":"Chang C, Zoback MD, Khaksar A (2006) Empirical relations between rock strength and physical properties in sedimentary rocks. J Pet Sci Eng 51:223\u2013237","journal-title":"J Pet Sci Eng"},{"key":"3344_CR2","unstructured":"Howard GC, Fast CR (1970) Hydraulic fracturing. Monograph, volume 2 of SPE, Henry L. Doherty Memorial Fund of AIME, Society of Petroleum Engineers of AIME"},{"issue":"3","key":"3344_CR3","doi-asserted-by":"publisher","first-page":"248","DOI":"10.2118\/18523-PA","volume":"5","author":"JM Gatens","year":"1990","unstructured":"Gatens JM, Harrison CW, Lancaster DE, Guldry FK (1990) In-situ stress tests and acoustic logs determine mechanical properties and stress profiles in the Devonian shales. SPE Form Eval 5(3):248\u2013254","journal-title":"SPE Form Eval"},{"issue":"4","key":"3344_CR4","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1016\/j.marpetgeo.2009.01.017","volume":"26","author":"MS Ameen","year":"2009","unstructured":"Ameen MS, Smart BG, Somerville JM, Hammilton S, Naji NA (2009) Predicting rock mechanical properties of carbonates from wireline logs (a case study: Arab-D reservoir, Ghawar field, Saudi Arabia). Mar Petrol Geol 26(4):430\u2013444","journal-title":"Mar Petrol Geol"},{"issue":"3\u20134","key":"3344_CR5","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.enggeo.2010.05.005","volume":"114","author":"AF Al-Anazi","year":"2010","unstructured":"Al-Anazi AF, 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":"3344_CR6","doi-asserted-by":"crossref","unstructured":"Barree RD, Gilbert JV, Conway MW (2009) Stress and rock property profiling for unconventional reservoir stimulation. Paper SPE 118703 presented at the SPE hydraulic fracturing technology conference, The Woodlands, Texas, 19\u201321 January","DOI":"10.2118\/118703-MS"},{"key":"3344_CR7","first-page":"1","volume":"9","author":"C Colin","year":"1997","unstructured":"Colin C, Potter S, Darren F (1997) Formation elastic parameters by deriving S-wave velocity logs. CREWES Research 9:1\u201310","journal-title":"CREWES Research"},{"key":"3344_CR8","unstructured":"Larsen I, Fj\u00e6r E, Renlie L (2000) Static and dynamic Poisson\u2019s ratio of weak sandstones. Paper ARMA-2000-0077 presented at the 4th North American rock mechanics symposium, Seattle, Washington, 31 July\u20133 August"},{"key":"3344_CR9","doi-asserted-by":"crossref","unstructured":"Abdulraheem A, Ahmed M, Vantala A, Parvez T (2009) Prediction of rock mechanical parameters for hydrocarbon reservoirs using different artificial intelligence techniques. Paper SPE 126094 presented at Saudi Arabia section technical symposium, Al-Khobar, Saudi Arabia, 9\u201311 May","DOI":"10.2118\/126094-MS"},{"key":"3344_CR10","volume-title":"Petroleum Related Rock Mechanics","author":"E Fjaer","year":"1992","unstructured":"Fjaer E, Holt RM, Horsrud P, Raaen AM, Risnes R (1992) Petroleum Related Rock Mechanics. Elsevier, Amsterdam"},{"key":"3344_CR11","unstructured":"King MS (1970) Static and dynamic elastic moduli of rocks under pressure. In: Proceedings of 11th U.S. symposium on rock mechanics, pp 329\u2013351"},{"issue":"1","key":"3344_CR12","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/0921-5093(93)90634-Q","volume":"165","author":"H Ledbetter","year":"1993","unstructured":"Ledbetter H (1993) Dynamic vs static Young\u2019s moduli a case study. Mater Sci Eng 165(1):9\u201310","journal-title":"Mater Sci Eng"},{"key":"3344_CR13","doi-asserted-by":"crossref","unstructured":"Canady WJ (2011) A method for full-range Young\u2019s Modulus correction. Paper SPE presented at 143604 North American unconventional gas conference and exhibition, The Woodlands, Texas, USA, 14\u201316 June","DOI":"10.2118\/143604-MS"},{"key":"3344_CR14","doi-asserted-by":"crossref","unstructured":"Khaksar A, Taylor PG, Fang Z, Kayes T, Salazar A, Rahman K (2009) Rock strength from core and logs, where we stand and ways to go. Paper SPE 121972 presented at the EUROPEC\/EAGE conference and exhibition, Amsterdam, The Netherlands","DOI":"10.2118\/121972-MS"},{"key":"3344_CR15","volume-title":"Elastic properties of rock minerals and rocks","author":"BP Belikov","year":"1970","unstructured":"Belikov BP, Alexandrov TW, Rysova TW (1970) Elastic properties of rock minerals and rocks. Nauka, Moscow"},{"key":"3344_CR16","volume-title":"Seismic methods in engineering geology","author":"NL Gorjainov","year":"1979","unstructured":"Gorjainov NL (1979) Seismic methods in engineering geology. Nedra, Moscow"},{"key":"3344_CR17","doi-asserted-by":"crossref","unstructured":"McCann, DM, Entwisle DC (1992) Determination of Young\u2019s Modulus of the rock mass from geophysical well logs. In: Hurst A, Giffiths CM, Worthington PF (eds) Geological applications of wireline logs II: Geological Society of Special Publications, vol 65, pp 317\u2013325","DOI":"10.1144\/GSL.SP.1992.065.01.24"},{"key":"3344_CR18","doi-asserted-by":"crossref","unstructured":"Morals RH, Marcinew RP (1993) Fracturing of high-permeability formations: mechanical properties correlations. SPE paper 26561, Presented in SPE annual technical conference and exhibition, Houston, Texas, 3\u20136 October","DOI":"10.2118\/26561-MS"},{"key":"3344_CR19","doi-asserted-by":"crossref","unstructured":"Bradford IDR, Fuller J, Thompson PJ, Walsgrove TR (1998) Benefits of assessing the solids production risk in a North Sea reservoir using elastoplastic modeling. Paper SPE-47360 presented at SPE\/ISRM rock mechanics in petroleum engineering, Trondheim, Norway, 8\u201310 July","DOI":"10.2118\/47360-MS"},{"issue":"5","key":"3344_CR20","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/0148-9062(83)90004-9","volume":"20","author":"MS King","year":"1983","unstructured":"King MS (1983) Static and dynamic elastic properties of rocks from the canadian shield. Int J Rock Mech Min Sci 20(5):237\u2013241","journal-title":"Int J Rock Mech Min Sci"},{"issue":"6","key":"3344_CR21","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1016\/0148-9062(88)90987-4","volume":"25","author":"EA Eissa","year":"1988","unstructured":"Eissa EA, Kazi A (1988) Relation between static and dynamic Young\u2019s modulus of rocks. Int J Rock Mech Min Sci Geomech 25(6):479\u2013482","journal-title":"Int J Rock Mech Min Sci Geomech"},{"key":"3344_CR22","first-page":"531","volume":"19","author":"Z Wang","year":"2000","unstructured":"Wang Z (2000) Dynamic versus static elastic properties of reservoir rocks, in seismic and acoustic velocities in reservoir rocks. SEG Geophys Reprint Ser 19:531\u2013539","journal-title":"SEG Geophys Reprint Ser"},{"issue":"2015","key":"3344_CR23","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.petrol.2014.12.010","volume":"126","author":"AR Najibi","year":"2015","unstructured":"Najibi AR, Mohammad G, Gholam RL, Mohammad RA (2015) Empirical relations between strength and static and dynamic elastic properties of Asmari and Sarvak limestones, two main oil reservoirs in Iran. J Petrol Sci Eng 126(2015):78\u201382","journal-title":"J Petrol Sci Eng"},{"key":"3344_CR24","unstructured":"Elkatatny SM, Mahmoud MA, Moahmed I, Abdulraheem A (2017) Development of a new correlation to determine the static Young\u2019s modulus. J Pet Explor Prod Technol, pp 1\u201310"},{"key":"3344_CR25","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.petrol.2016.04.011","volume":"146","author":"MA Mahmoud","year":"2016","unstructured":"Mahmoud MA, Elkatatny SA, Ramadan E, Abdulraheem A (2016) Development of lithology-based static Young\u2019s modulus correlations from log data based on data clustering technique. J Pet Sci Eng 146:10\u201320","journal-title":"J Pet Sci Eng"},{"key":"3344_CR26","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.cageo.2011.08.001","volume":"41","author":"A \u00c1lvarez del Castillo","year":"2012","unstructured":"\u00c1lvarez del Castillo A, Santoyo E, Garc\u00eda-Valladares O (2012) \u0391 new void fraction correlation inferred from artificial neural networks for modeling two-phase flows in geothermal wells. Comput Geosci 41:25\u201339. \n                    https:\/\/doi.org\/10.1016\/j.cageo.2011.08.001","journal-title":"Comput Geosci"},{"key":"3344_CR27","doi-asserted-by":"publisher","unstructured":"Lippman RP, Lippman RP (1987) An introduction to computing with neural nets. In: Mag A (ed) IEEE ASSP magazine IEEE, pp 4\u201322. \n                    https:\/\/doi.org\/10.1109\/massp.1987.1165576","DOI":"10.1109\/massp.1987.1165576"},{"key":"3344_CR28","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1016\/S0895-4356(97)00163-7","volume":"50","author":"P Vineis","year":"1997","unstructured":"Vineis P, Rainoldi A (1997) Neural networks and logistic regression: analysis of a case-control study on myocardial infarction. J Clin Epidemiol 50:1309\u20131310. \n                    https:\/\/doi.org\/10.1016\/S0895-4356(97)00163-7","journal-title":"J Clin Epidemiol"},{"key":"3344_CR29","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/S0097-8485(01)00094-8","volume":"26","author":"R Burbidge","year":"2001","unstructured":"Burbidge R, Trotter M, Buxton B, Holden S (2001) Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput Chem 26:5\u201314. \n                    https:\/\/doi.org\/10.1016\/S0097-8485(01)00094-8","journal-title":"Comput Chem"},{"key":"3344_CR30","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527\u20131554. \n                    https:\/\/doi.org\/10.1162\/neco.2006.18.7.1527","journal-title":"Neural Comput"},{"key":"3344_CR31","doi-asserted-by":"publisher","unstructured":"Cranganu C, Breaban ME, Luchian H (2015) Artificial intelligent approaches in petroleum geosciences, Artificial intelligent approaches in petroleum geosciences. Springer International Publishing, Cham. \n                    https:\/\/doi.org\/10.1007\/978-3-319-16531-8","DOI":"10.1007\/978-3-319-16531-8"},{"key":"3344_CR32","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1109\/21.256541","volume":"23","author":"J-SR Jang","year":"1993","unstructured":"Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665\u2013685. \n                    https:\/\/doi.org\/10.1109\/21.256541","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"3344_CR33","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, Hezarkhani A (2012) A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Comput Geosci 42:18\u201327. \n                    https:\/\/doi.org\/10.1016\/j.cageo.2012.02.004","journal-title":"Comput Geosci"},{"key":"3344_CR34","doi-asserted-by":"publisher","first-page":"32","DOI":"10.5120\/ijca2015905635","volume":"123","author":"N Walia","year":"2015","unstructured":"Walia N, Singh H, Sharma A (2015) ANFIS: adaptive neuro-fuzzy inference system\u2014a survey. Int J Comput Appl 123:32\u201338. \n                    https:\/\/doi.org\/10.5120\/ijca2015905635","journal-title":"Int J Comput Appl"},{"key":"3344_CR35","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1007\/s00521-012-0942-1","volume":"23","author":"T U\u00e7ar","year":"2013","unstructured":"U\u00e7ar T, Karahoca A, Karahoca D (2013) Tuberculosis disease diagnosis by using adaptive neuro fuzzy inference system and rough sets. Neural Comput Appl 23:471\u2013483. \n                    https:\/\/doi.org\/10.1007\/s00521-012-0942-1","journal-title":"Neural Comput Appl"},{"key":"3344_CR36","doi-asserted-by":"publisher","unstructured":"Guo G (2014) Support vector machines applications. In: Ma Y, Guo G (eds) Support vector machines applications. Springer International Publishing, Cham. \n                    https:\/\/doi.org\/10.1007\/978-3-319-02300-7","DOI":"10.1007\/978-3-319-02300-7"},{"key":"3344_CR37","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/S0165-0114(02)00570-5","volume":"138","author":"J-T Jeng","year":"2003","unstructured":"Jeng J-T, Chuang C-C, Su S-F (2003) Support vector interval regression networks for interval regression analysis. Fuzzy Sets Syst 138:283\u2013300. \n                    https:\/\/doi.org\/10.1016\/S0165-0114(02)00570-5","journal-title":"Fuzzy Sets Syst"},{"key":"3344_CR38","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1142\/S1469026811003100","volume":"10","author":"A Khoukhi","year":"2011","unstructured":"Khoukhi A, Oloso M, Elshafei M, Abdulraheem A, Al-Majed A (2011) SUPPORT vector regression and functional networks for viscosity and gas\/oil ratio curves estimation. Int J Comput Intell Appl 10:269\u2013293. \n                    https:\/\/doi.org\/10.1142\/S1469026811003100","journal-title":"Int J Comput Intell Appl"},{"key":"3344_CR39","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1016\/j.petrol.2016.08.021","volume":"146","author":"SM Elkatatny","year":"2016","unstructured":"Elkatatny SM, Tariq Z, Mahmoud MA (2016) Real time prediction of drilling fluid rheological properties using artificial neural networks visible mathematical model (white box). J Petrol Sci Eng 146:1202\u20131210","journal-title":"J Petrol Sci Eng"},{"issue":"4","key":"3344_CR40","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1007\/s13369-016-2409-7","volume":"42","author":"SM Elkatatny","year":"2017","unstructured":"Elkatatny SM (2017) Real time prediction of rheological parameters of KCl water-based drilling fluid using artificial neural networks. Arab J Sci Eng 42(4):1655\u20131665","journal-title":"Arab J Sci Eng"},{"key":"3344_CR41","doi-asserted-by":"crossref","unstructured":"Elkatatny SM, Mahmoud M (2017) Development of new correlations for the oil formation volume factor in oil reservoirs using artificial intelligent white box technique. Petroleum (in press)","DOI":"10.1016\/j.petlm.2017.09.009"},{"key":"3344_CR42","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-017-2589-9","author":"SM Elkatatny","year":"2017","unstructured":"Elkatatny SM, Mahmoud M (2017) Development of a new correlation for bubble point pressure in oil reservoirs using artificial intelligent white box technique. Arab J Sci Eng. \n                    https:\/\/doi.org\/10.1007\/s13369-017-2589-9","journal-title":"Arab J Sci Eng"},{"key":"3344_CR43","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-017-2850-x","author":"SM Elkatatny","year":"2017","unstructured":"Elkatatny SM, Mahmoud MA, Tariq Z, Abdulraheem A (2017) New insights into the prediction of heterogeneous carbonate reservoir permeability from well logs using artificial intelligent network. Neural Comput Appl. \n                    https:\/\/doi.org\/10.1007\/s00521-017-2850-x","journal-title":"Neural Comput Appl"},{"key":"3344_CR44","unstructured":"Bandar AD, Algarni MT, Tale M, Almushiqeh I (2011) Prediction of Poisson\u2019s ratio and Young\u2019s modulus for hydrocarbon reservoirs using alternating conditional expectation algorithm. Paper SPE 138841 presented at SPE Middle east oil and gas show and conference, Manama, Bahrain, 25\u201328 September"},{"key":"3344_CR45","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.measurement.2016.03.050","volume":"88","author":"N Madhubabu","year":"2016","unstructured":"Madhubabu N, Singh PK, Kainthola A, Mahanta B, Tripathy A, Singh TN (2016) Prediction of compressive strength and elastic modulus of carbonate rocks. Measurement 88:202\u2013213","journal-title":"Measurement"},{"key":"3344_CR46","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.ijrmms.2013.08.004","volume":"63","author":"M Beiki","year":"2013","unstructured":"Beiki M, Majdi A, Givshad AD (2013) Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks. Int J Rock Mech Min Sci 63:159\u2013169","journal-title":"Int J Rock Mech Min Sci"},{"issue":"1","key":"3344_CR47","first-page":"41","volume":"20","author":"S Dehghan","year":"2010","unstructured":"Dehghan S, Sattari G, Chelgani SC, Aliabadi M (2010) Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Min Sci Technol 20(1):41\u201346","journal-title":"Min Sci Technol"},{"key":"3344_CR48","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.jngse.2016.10.033","volume":"36","author":"S Yin","year":"2016","unstructured":"Yin S, Ding W, Shan Y, Zhou W, Wang R, Zhou X, Li A, He J (2016) A new method for assessing Young\u2019s modulus and Poisson\u2019s ratio in tight interbedded clastic reservoirs without a shear wave time difference. J Nat Gas Sci Eng 36:267\u2013279","journal-title":"J Nat Gas Sci Eng"},{"key":"3344_CR49","doi-asserted-by":"crossref","unstructured":"Ghasemi E, Kalhori H, Bagherpour R, Yagiz S (2016) Model tree approach for predicting uniaxial compressive strength and Young\u2019s modulus of carbonate rocks. Bull Eng Geol Environ (in press)","DOI":"10.1007\/s10064-016-0931-1"},{"key":"3344_CR50","doi-asserted-by":"publisher","unstructured":"Aboutaleb S, Behnia M, Bagherpour R, Bluekian B (2017) Using non-destructive tests for estimating uniaxial compressive strength and static Young\u2019s modulus of carbonate rocks via some modeling techniques. Bull Eng Geol Environ 1\u201317. \n                    https:\/\/doi.org\/10.1007\/s10064-017-1043-2","DOI":"10.1007\/s10064-017-1043-2"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-018-3344-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3344-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3344-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,6]],"date-time":"2019-09-06T14:27:16Z","timestamp":1567780036000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-018-3344-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,13]]},"references-count":50,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2019,8]]}},"alternative-id":["3344"],"URL":"https:\/\/doi.org\/10.1007\/s00521-018-3344-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,13]]},"assertion":[{"value":"5 September 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that there is no conflict of interests regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}