{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T11:04:53Z","timestamp":1767611093399,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030228071"},{"type":"electronic","value":"9783030228088"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-22808-8_24","type":"book-chapter","created":{"date-parts":[[2019,6,26]],"date-time":"2019-06-26T00:02:30Z","timestamp":1561507350000},"page":"232-241","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Artificial Neural Network Surrogate Modeling of Oil Reservoir: A Case Study"],"prefix":"10.1007","author":[{"given":"Oleg","family":"Sudakov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dmitri","family":"Koroteev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boris","family":"Belozerov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evgeny","family":"Burnaev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,6,26]]},"reference":[{"key":"24_CR1","unstructured":"Computer modelling group ltd. https:\/\/www.cmgl.ca\/software"},{"key":"24_CR2","unstructured":"Eclipse reservoir simulator. https:\/\/www.software.slb.com"},{"key":"24_CR3","unstructured":"TNavigator - rock flow dynamics. http:\/\/rfdyn.com\/tnavigator\/"},{"key":"24_CR4","unstructured":"Alestra, S., et al.: Surrogate models for spacecraft aerodynamic problems. In: Proceedings of WCCM - ECCM - ECFD 2014 Congress, Barcelona, Spain, 20\u201325 July 2014"},{"issue":"3","key":"24_CR5","first-page":"114","volume":"7","author":"M Belyaev","year":"2013","unstructured":"Belyaev, M., Burnaev, E.: Approximation of a multidimensional dependency based on a linear expansion in a dictionary of parametric functions. Inform. Appl. 7(3), 114\u2013125 (2013)","journal-title":"Inform. Appl."},{"key":"24_CR6","doi-asserted-by":"crossref","first-page":"405","DOI":"10.4028\/www.scientific.net\/AMR.1016.405","volume":"1016","author":"M Belyaev","year":"2014","unstructured":"Belyaev, M., et al.: Building data fusion surrogate models for spacecraft aerodynamic problems with incomplete factorial design of experiments. Adv. Mater. Res. 1016, 405\u2013412 (2014)","journal-title":"Adv. Mater. Res."},{"key":"24_CR7","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.advengsoft.2016.09.001","volume":"102","author":"M Belyaev","year":"2016","unstructured":"Belyaev, M., et al.: Gtapprox: surrogate modeling for industrial design. Adv. Eng. Softw. 102, 29\u201339 (2016)","journal-title":"Adv. Eng. Softw."},{"issue":"6","key":"24_CR8","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1134\/S106422691606005X","volume":"61","author":"E Burnaev","year":"2016","unstructured":"Burnaev, E., Erofeev, P.: The influence of parameter initialization on the training time and accuracy of a nonlinear regression model. J. Comm. Tech. and Electr. 61(6), 646\u2013660 (2016)","journal-title":"J. Comm. Tech. and Electr."},{"key":"24_CR9","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1007\/978-3-319-17091-6_7","volume-title":"Statistical Learning and Data Sciences","author":"E Burnaev","year":"2015","unstructured":"Burnaev, E., Panov, M.: Adaptive design of experiments based on gaussian processes. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds.) SLDS 2015. LNCS (LNAI), vol. 9047, pp. 116\u2013125. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-17091-6_7"},{"issue":"10","key":"24_CR10","doi-asserted-by":"crossref","first-page":"1630","DOI":"10.1134\/S0005117913100044","volume":"74","author":"E Burnaev","year":"2013","unstructured":"Burnaev, E., Prikhodko, P.: On a method for constructing ensembles of regression models. Autom. Remote Control 74(10), 1630\u20131644 (2013)","journal-title":"Autom. Remote Control"},{"key":"24_CR11","volume-title":"Mathematical Models and Finite Elements for Reservoir Simulation: Single Phase, Multiphase and Multicomponent Flows Through Porous Media","author":"G Chavent","year":"1986","unstructured":"Chavent, G., Jaffr\u00e9, J.: Mathematical Models and Finite Elements for Reservoir Simulation: Single Phase, Multiphase and Multicomponent Flows Through Porous Media, vol. 17. Elsevier, Amsterdam (1986)"},{"key":"24_CR12","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/978-1-4614-7551-4_15","volume-title":"Surrogate-Based Modeling and Optimization","author":"S Grihon","year":"2013","unstructured":"Grihon, S., Burnaev, E., Belyaev, M., Prikhodko, P.: Surrogate modeling of stability constraints for optimization of composite structures. In: Koziel, S., Leifsson, L. (eds.) Surrogate-Based Modeling and Optimization, pp. 359\u2013391. Springer, New York (2013). https:\/\/doi.org\/10.1007\/978-1-4614-7551-4_15"},{"key":"24_CR13","series-title":"Springer Series in Statistics","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.: The Elements of Statistical Learning. SSS, 2nd edn. Springer, New York (2009). https:\/\/doi.org\/10.1007\/978-0-387-84858-7","edition":"2"},{"key":"24_CR14","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.jngse.2015.04.018","volume":"25","author":"A Kalantari-Dahaghi","year":"2015","unstructured":"Kalantari-Dahaghi, A., Mohaghegh, S., Esmaili, S.: Coupling numerical simulation and machine learning to model shale gas production at different time resolutions. J. Nat. Gas Sci. Eng. 25, 380\u2013392 (2015)","journal-title":"J. Nat. Gas Sci. Eng."},{"key":"24_CR15","doi-asserted-by":"crossref","unstructured":"Klyuchnikov, N., et al.: Data-driven model for the identification of the rock type at a drilling bit. J. Petrol. Sci. Eng. (2019)","DOI":"10.1016\/j.petrol.2019.03.041"},{"issue":"1","key":"24_CR16","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s11081-017-9351-8","volume":"18","author":"L Lasdon","year":"2017","unstructured":"Lasdon, L., Shirzadi, S., Ziegel, E.: Implementing crm models for improved oil recovery in large oil fields. Opt. Engin. 18(1), 87\u2013103 (2017)","journal-title":"Opt. Engin."},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Makhotin, I., Burnaev, E., Koroteev, D.: Gradient boosting to boost the efficiency of hydraulic fracturing. J. Petrol. Explor. Prod. Tech. (2019)","DOI":"10.1007\/s13202-019-0636-7"},{"issue":"8","key":"24_CR18","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/S0098-3004(00)00029-7","volume":"26","author":"S Mohaghegh","year":"2000","unstructured":"Mohaghegh, S., Popa, A., Ameri, S.: Design optimum frac jobs using virtual intelligence techniques. Comput. Geosci. 26(8), 927\u2013939 (2000)","journal-title":"Comput. Geosci."},{"issue":"2","key":"24_CR19","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1504\/IJOGCT.2014.059284","volume":"7","author":"SD Mohaghegh","year":"2014","unstructured":"Mohaghegh, S.D.: Converting detail reservoir simulation models into effective reservoir management tools using SRMs; case study-three green fields in saudi arabia. Int. J. Oil Gas Coal Technol. 7(2), 115\u2013131 (2014)","journal-title":"Int. J. Oil Gas Coal Technol."},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Mohaghegh, S.D., Amini, S., Gholami, V., Gaskari, R., Bromhal, G.S., et al.: Grid-based surrogate reservoir modeling (SRM) for fast track analysis of numerical reservoir simulation models at the gridblock level. In: SPE Western Regional Meeting. Society of Petroleum Engineers (2012)","DOI":"10.2118\/153844-MS"},{"key":"24_CR21","unstructured":"Nguyen, A.P.: Capacitance resistance modeling for primary recovery, waterflood and water-CO flood. Ph.D. thesis (2012)"},{"key":"24_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/978-3-319-73013-4_23","volume-title":"Analysis of Images, Social Networks and Texts","author":"A Notchenko","year":"2018","unstructured":"Notchenko, A., Kapushev, Y., Burnaev, E.: Large-scale shape retrieval with sparse 3D convolutional neural networks. In: van der Aalst, W.M.P., Ignatov, D.I., Khachay, M., Kuznetsov, S.O., Lempitsky, V., Lomazova, I.A., Loukachevitch, N., Napoli, A., Panchenko, A., Pardalos, P.M., Savchenko, A.V., Wasserman, S. (eds.) AIST 2017. LNCS, vol. 10716, pp. 245\u2013254. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-73013-4_23"},{"key":"24_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/978-3-319-17091-6_4","volume-title":"Statistical Learning and Data Sciences","author":"E Burnaev","year":"2015","unstructured":"Burnaev, E., Panin, I.: Adaptive design of experiments for sobol indices estimation based on quadratic metamodel. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds.) SLDS 2015. LNCS (LNAI), vol. 9047, pp. 86\u201395. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-17091-6_4"},{"key":"24_CR24","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-3-319-33395-3_12","volume-title":"Conformal and Probabilistic Prediction with Applications","author":"E Burnaev","year":"2016","unstructured":"Burnaev, E., Panin, I., Sudret, B.: Effective design for sobol indices estimation based on polynomial chaos expansions. In: Gammerman, A., Luo, Z., Vega, J., Vovk, V. (eds.) COPA 2016. LNCS (LNAI), vol. 9653, pp. 165\u2013184. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-33395-3_12"},{"issue":"1\u20132","key":"24_CR25","first-page":"187","volume":"81","author":"I Panin","year":"2017","unstructured":"Panin, I., Sudret, B., Burnaev, E.: Efficient design of experiments for sensitivity analysis based on polynomial chaos expansions. Ann. Math. Artif. Intell. 81(1\u20132), 187\u2013207 (2017)","journal-title":"Ann. Math. Artif. Intell."},{"key":"24_CR26","volume-title":"Geostatistical Reservoir Modeling","author":"MJ Pyrcz","year":"2014","unstructured":"Pyrcz, M.J., Deutsch, C.V.: Geostatistical Reservoir Modeling. Oxford University Press, Oxford (2014)"},{"key":"24_CR27","doi-asserted-by":"crossref","first-page":"85","DOI":"10.4028\/www.scientific.net\/AMR.1016.85","volume":"1016","author":"G Sterling","year":"2014","unstructured":"Sterling, G., Prikhodko, P., Burnaev, E., Belyaev, M., Grihon, S.: On approximation of reserve factors dependency on loads for composite stiffened panels. Adv. Mater. Res. 1016, 85\u201389 (2014)","journal-title":"Adv. Mater. Res."},{"key":"24_CR28","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.cageo.2019.02.002","volume":"127","author":"O Sudakov","year":"2019","unstructured":"Sudakov, O., Burnaev, E., Koroteev, D.: Driving digital rock towards machine learning: predicting permeability with gradient boosting and deep neural networks. Comput. Geosci. 127, 91\u201398 (2019)","journal-title":"Comput. Geosci."},{"key":"24_CR29","doi-asserted-by":"crossref","unstructured":"Temirchev, P., et al.: Deep neural networks predicting oil movement in a development unit. J. Petrol Sci. Eng. (2019)","DOI":"10.1016\/j.petrol.2019.106513"},{"key":"24_CR30","unstructured":"Tyson, S.: An Introduction to Reservoir Modelling. Piper\u2019s Ash Limited, United Kingdom (2007)"}],"container-title":["Lecture Notes in Computer Science","Advances in Neural Networks \u2013 ISNN 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-22808-8_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:15:45Z","timestamp":1710260145000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-22808-8_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030228071","9783030228088"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-22808-8_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"26 June 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISNN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Moscow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 July 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 July 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isnn2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conference.cs.cityu.edu.hk\/isnn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}