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Machine learning-driven permeability prediction in carbonates and sandstones using NMR relaxation data.

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Title: Machine learning-driven permeability prediction in carbonates and sandstones using NMR relaxation data.
Authors: Kellal, Sara1, g202212360@kfupm.edu.sa, Nandito, Davy1, El-Husseiny, Ammar1,2, ammar.elhusseiny@kfupm.edu.sa, Hassan, Amjed2, Hanafy, Sherif M.1,2
Source: Artificial Intelligence in Geosciences; 2026, Vol. 7 Issue 1, p1-16, 16p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 193003216
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: <searchLink fieldCode="AU" term="%22Kellal%2C+Sara%22">Kellal, Sara</searchLink><relatesTo>1</relatesTo>, <i>g202212360@kfupm.edu.sa</i><br /><searchLink fieldCode="AU" term="%22Nandito%2C+Davy%22">Nandito, Davy</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AU" term="%22El-Husseiny%2C+Ammar%22">El-Husseiny, Ammar</searchLink><relatesTo>1,2</relatesTo>, <i>ammar.elhusseiny@kfupm.edu.sa</i><br /><searchLink fieldCode="AU" term="%22Hassan%2C+Amjed%22">Hassan, Amjed</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AU" term="%22Hanafy%2C+Sherif+M%2E%22">Hanafy, Sherif M.</searchLink><relatesTo>1,2</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Artificial+Intelligence+in+Geosciences%22">Artificial Intelligence in Geosciences</searchLink>; 2026, Vol. 7 Issue 1, p1-16, 16p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=193003216
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.aiig.2026.100193
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      – Code: eng
        Text: English
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        PageCount: 16
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      – TitleFull: Machine learning-driven permeability prediction in carbonates and sandstones using NMR relaxation data.
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            NameFull: Kellal, Sara
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            NameFull: Nandito, Davy
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            NameFull: El-Husseiny, Ammar
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            NameFull: Hassan, Amjed
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              M: 06
              Text: 2026
              Type: published
              Y: 2026
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              Value: 7
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