Permeability Prediction and Diagenesis in Tight Carbonates Using Machine Learning Techniques

Al Khalifa, H, Glover, PWJ orcid.org/0000-0003-1715-5474 and Lorinczi, P (2020) Permeability Prediction and Diagenesis in Tight Carbonates Using Machine Learning Techniques. Marine and Petroleum Geology, 112. 104096. ISSN 0264-8172

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2019 Elsevier Ltd. This is an author produced version of a paper published in Marine and Petroleum Geology. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Permeability; Neural networks; Genetic algorithms; Machine learning; Tight carbonates; MICP; Porosity; Diagenesis
Dates:
  • Published: February 2020
  • Accepted: 16 October 2019
  • Published (online): 23 October 2019
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Institute for Applied Geosciences (IAG) (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 17 Oct 2019 15:47
Last Modified: 06 Dec 2019 12:51
Status: Published
Publisher: Elsevier
Identification Number: https://doi.org/10.1016/j.marpetgeo.2019.104096

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