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Numerical simulations of NMR relaxation in chalk using local Robin boundary conditions
Örebro University, School of Science and Technology. Nano-Science Center, Department of Chemistry, University of Copenhagen, Denmark.ORCID iD: 0000-0002-2630-7479
Nano-Science Center, Department of Chemistry, University of Copenhagen, Denmark..
Nano-Science Center, Department of Chemistry, University of Copenhagen, Denmark..
Nano-Science Center, Department of Chemistry, University of Copenhagen, Denmark..
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2019 (English)In: Journal of magnetic resonance, ISSN 1090-7807, E-ISSN 1096-0856, Vol. 308, article id 106597Article in journal (Refereed) Published
Abstract [en]

The interpretation of nuclear magnetic resonance (NMR) data is of interest in a number of fields. In Ögren [Eur. Phys. J. B (2014) 87: 255] local boundary conditions for random walk simulations of NMR relaxation in digital domains were presented. Here, we have applied those boundary conditions to large, three-dimensional (3D) porous media samples. We compared the random walk results with known solutions and then applied them to highly structured 3D domains, from images derived using synchrotron radiation CT scanning of North Sea chalk samples. As expected, there were systematic errors caused by digitalization of the pore surfaces so we quantified those errors, and by using linear local boundary conditions, we were able to significantly improve the output. We also present a technique for treating numerical data prior to input into the ESPRIT algorithm for retrieving Laplace components of time series from NMR data (commonly called T-inversion).

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 308, article id 106597
Keywords [en]
NMR-relaxation, random walk, boundary conditions, CT-scanning, T-inversion
National Category
Geophysics Computational Mathematics
Research subject
Physics; Mathematics
Identifiers
URN: urn:nbn:se:oru:diva-76669DOI: 10.1016/j.jmr.2019.106597ISI: 000495003900019Scopus ID: 2-s2.0-85072525625OAI: oai:DiVA.org:oru-76669DiVA, id: diva2:1353694
Note

Funding Agencies:

Innovation Fund Denmark through the project P3 - Predicting Petrophysical Parameters  

Maersk Oil through the project P3 - Predicting Petrophysical Parameters 

Available from: 2019-09-23 Created: 2019-09-23 Last updated: 2020-01-16Bibliographically approved

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Ögren, MagnusGulliksson, Mårten

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