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Quantum Bayesian perspective for intelligence reservoir characterization, monitoring and management
Aseguramiento Tecnol Pemex Explorac & Prod, Mexico.
Linnaeus University, Faculty of Technology, Department of Mathematics. (Internat Ctr Math Modelling Phys & Cognit Sci)ORCID iD: 0000-0002-9857-0938
UNAM, Mexico.
Suptcia Caracterizac Yacimientos, Mexico.
2017 (English)In: Philosophical Transactions. Series A: Mathematical, physical, and engineering science, ISSN 1364-503X, E-ISSN 1471-2962, Vol. 375, no 2106, 20160398Article in journal (Refereed) 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'.

Place, publisher, year, edition, pages
2017. Vol. 375, no 2106, 20160398
Keyword [en]
quantum Bayesian inference, uncertainty, geophysical data, contextuality, intelligent hydrocarbon reservoir
National Category
Mathematics
Research subject
Natural Science, Mathematics
Identifiers
URN: urn:nbn:se:lnu:diva-68550DOI: 10.1098/rsta.2016.0398ISI: 000412179900014OAI: oai:DiVA.org:lnu-68550DiVA: diva2:1154205
Available from: 2017-11-01 Created: 2017-11-01 Last updated: 2017-11-01Bibliographically approved

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Khrennikov, Andrei
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CiteExportLink to record
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