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001434452 001__ 1434452
001434452 003__ OCoLC
001434452 005__ 20230309003730.0
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001434452 007__ cr\nn\nnnunnun
001434452 008__ 210131s2021\\\\sz\a\\\\ob\\\\001\0\eng\d
001434452 019__ $$a1235870217$$a1236260628$$a1244120692
001434452 020__ $$a9783030674748$$q(electronic book)
001434452 020__ $$a3030674746$$q(electronic book)
001434452 020__ $$z3030674738
001434452 020__ $$z9783030674731
001434452 0247_ $$a10.1007/978-3-030-67474-8$$2doi
001434452 035__ $$aSP(OCoLC)1239997532
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001434452 049__ $$aISEA
001434452 050_4 $$aTN271.P4
001434452 08204 $$a622/.1828$$223
001434452 1001_ $$aUrsegov, Stanislav,$$eauthor$$1https://orcid.org/0000-0002-8648-5496
001434452 24510 $$aAdaptive approach to petroleum reservoir simulation /$$cStanislav Ursegov, Armen Zakharian.
001434452 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2021]
001434452 300__ $$a1 online resource (ix, 86 pages) :$$billustrations (some color)
001434452 336__ $$atext$$btxt$$2rdacontent
001434452 337__ $$acomputer$$bc$$2rdamedia
001434452 338__ $$aonline resource$$bcr$$2rdacarrier
001434452 347__ $$atext file
001434452 347__ $$bPDF
001434452 4901_ $$aAdvances in oil and gas exploration & production,$$x2509-372X
001434452 504__ $$aIncludes bibliographical references and index.
001434452 5050_ $$aIntroduction -- Information capacity of initial data -- Contrasts between adaptive and deterministic models -- Alternatives for mathematical apparatus of adaptive simulation : neural networks and fuzzy logic -- Adaptive geological modeling -- Adaptive hydrodynamic modeling -- Adaptive forecasting -- Adaptive software system Cervart -- Conclusion.
001434452 506__ $$aAccess limited to authorized users.
001434452 520__ $$aThis book presents unique features of the adaptive modeling approach based on new machine learning algorithms for petroleum exploration, development, and production. The adaptive approach helps simulation engineers and geoscientists to create adequate geological and hydrodynamic models. This approach is proven to be a real alternative to traditional techniques, such as deterministic modeling. Currently, machine-learning algorithms grow in popularity because they provide consistency, predictiveness, and convenience. The primary purpose of this book is to describe the theoretical state of the adaptive approach and show some examples of its implementation in simulation and forecasting different reservoir processes.
001434452 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 12, 2021).
001434452 650_0 $$aPetroleum$$xProspecting$$xData processing.
001434452 650_0 $$aOil fields$$xComputer simulation.
001434452 650_0 $$aMachine learning.
001434452 650_6 $$aPétrole$$xProspection$$xInformatique.
001434452 650_6 $$aGisements pétrolifères$$xSimulation par ordinateur.
001434452 650_6 $$aApprentissage automatique.
001434452 655_0 $$aElectronic books.
001434452 7001_ $$aZakharian, Armen,$$eauthor.
001434452 77608 $$iPrint version:$$z9783030674731
001434452 830_0 $$aAdvances in oil and gas exploration & production,$$x2509-372X
001434452 852__ $$bebk
001434452 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-67474-8$$zOnline Access$$91397441.1
001434452 909CO $$ooai:library.usi.edu:1434452$$pGLOBAL_SET
001434452 980__ $$aBIB
001434452 980__ $$aEBOOK
001434452 982__ $$aEbook
001434452 983__ $$aOnline
001434452 994__ $$a92$$bISE