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Quantum Bayesian perspective for intelligence reservoir characterization, monitoring and management
Aseguramiento Tecnol Pemex Explorac & Prod, Mexico.
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för matematik (MA). (Int Ctr Math Modeling Phys & Cognit Sci)ORCID-id: 0000-0002-9857-0938
UNAM, Mexico.
Suptcia Caracterizac Yacimientos, Mexico.
2017 (Engelska)Ingår i: Philosophical Transactions. Series A: Mathematical, physical, and engineering science, ISSN 1364-503X, E-ISSN 1471-2962, Vol. 375, nr 2106, artikel-id 20160398Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The paper starts with a brief review of the literature about uncertainty in geological, geophysical and petrophysical data. In particular, we present the viewpoints of experts in geophysics on the application of Bayesian inference and subjective probability. Then we present arguments that the use of classical probability theory (CP) does not match completely the structure of geophysical data. We emphasize that such data are characterized by contextuality and non-Kolmogorovness (the impossibility to use the CP model), incompleteness as well as incompatibility of some geophysical measurements. These characteristics of geophysical data are similar to the characteristics of quantum physical data. Notwithstanding all this, contextuality can be seen as a major deviation of quantum theory from classical physics. In particular, the contextual probability viewpoint is the essence of the Vaxjo interpretation of quantum mechanics. We propose to use quantum probability (QP) for decision-making during the characterization, modelling, exploring and management of the intelligent hydrocarbon reservoir. Quantum Bayesianism (QBism), one of the recently developed information interpretations of quantum theory, can be used as the interpretational basis for such QP decision-making in geology, geophysics and petroleum projects design and management. This article is part of the themed issue ` Second quantum revolution: foundational questions'.

Ort, förlag, år, upplaga, sidor
Royal Society Publishing , 2017. Vol. 375, nr 2106, artikel-id 20160398
Nyckelord [en]
quantum Bayesian inference, uncertainty, geophysical data, contextuality, intelligent hydrocarbon reservoir
Nationell ämneskategori
Matematik
Forskningsämne
Naturvetenskap, Matematik
Identifikatorer
URN: urn:nbn:se:lnu:diva-68550DOI: 10.1098/rsta.2016.0398ISI: 000412179900014Scopus ID: 2-s2.0-85030569634OAI: oai:DiVA.org:lnu-68550DiVA, id: diva2:1154205
Tillgänglig från: 2017-11-01 Skapad: 2017-11-01 Senast uppdaterad: 2019-09-06Bibliografiskt granskad

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Khrennikov, Andrei

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