Corrigendum to “Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images” [Geoenergy Sci. Eng. (2023), Vol 230, 212160] (S2949891023007479), (10.1016/j.geoen.2023.212160)

Siyan Liu; Ming Fan; Dan Lu

doi:10.1016/j.geoen.2023.212514

Corrigendum to “Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images” [Geoenergy Sci. Eng. (2023), Vol 230, 212160] (S2949891023007479), (10.1016/j.geoen.2023.212160)

Siyan Liu, Ming Fan, Dan Lu

Computational Earth Sciences Group

Research output: Contribution to journal › Comment/debate

Abstract

The authors would like to extend their heartfelt thanks to Dr. Chi Zhang of the Department of Meteorology and Geophysics at the University of Vienna, Austria, and Dr. Reza Barati of the Department of Chemical & Petroleum Engineering at the University of Kansas, USA. Their insightful discussions and ideas were vital in shaping the initial direction of our research. Their expertise and innovative perspectives have added substantial value to our work, and for this, we are profoundly grateful. The authors would like to apologise for any inconvenience caused.

Original language	English
Article number	212514
Journal	Geoenergy Science and Engineering
Volume	237
DOIs	https://doi.org/10.1016/j.geoen.2023.212514
State	Published - Jun 2024

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Liu, S., Fan, M., & Lu, D. (2024). Corrigendum to “Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images” [Geoenergy Sci. Eng. (2023), Vol 230, 212160] (S2949891023007479), (10.1016/j.geoen.2023.212160). Geoenergy Science and Engineering, 237, Article 212514. https://doi.org/10.1016/j.geoen.2023.212514

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title = "Corrigendum to “Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images” [Geoenergy Sci. Eng. (2023), Vol 230, 212160] (S2949891023007479), (10.1016/j.geoen.2023.212160)",

abstract = "The authors would like to extend their heartfelt thanks to Dr. Chi Zhang of the Department of Meteorology and Geophysics at the University of Vienna, Austria, and Dr. Reza Barati of the Department of Chemical & Petroleum Engineering at the University of Kansas, USA. Their insightful discussions and ideas were vital in shaping the initial direction of our research. Their expertise and innovative perspectives have added substantial value to our work, and for this, we are profoundly grateful. The authors would like to apologise for any inconvenience caused.",

author = "Siyan Liu and Ming Fan and Dan Lu",

note = "Publisher Copyright: {\textcopyright} 2023 Elsevier B.V.",

year = "2024",

month = jun,

doi = "10.1016/j.geoen.2023.212514",

language = "English",

volume = "237",

journal = "Geoenergy Science and Engineering",

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Liu, S , Fan, M & Lu, D 2024, 'Corrigendum to “Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images” [Geoenergy Sci. Eng. (2023), Vol 230, 212160] (S2949891023007479), (10.1016/j.geoen.2023.212160)', Geoenergy Science and Engineering, vol. 237, 212514. https://doi.org/10.1016/j.geoen.2023.212514

Corrigendum to “Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images” [Geoenergy Sci. Eng. (2023), Vol 230, 212160] (S2949891023007479), (10.1016/j.geoen.2023.212160). / Liu, Siyan ; Fan, Ming ; Lu, Dan.
In: Geoenergy Science and Engineering, Vol. 237, 212514, 06.2024.

Research output: Contribution to journal › Comment/debate

TY - JOUR

T1 - Corrigendum to “Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images” [Geoenergy Sci. Eng. (2023), Vol 230, 212160] (S2949891023007479), (10.1016/j.geoen.2023.212160)

AU - Liu, Siyan

AU - Fan, Ming

AU - Lu, Dan

PY - 2024/6

Y1 - 2024/6

N2 - The authors would like to extend their heartfelt thanks to Dr. Chi Zhang of the Department of Meteorology and Geophysics at the University of Vienna, Austria, and Dr. Reza Barati of the Department of Chemical & Petroleum Engineering at the University of Kansas, USA. Their insightful discussions and ideas were vital in shaping the initial direction of our research. Their expertise and innovative perspectives have added substantial value to our work, and for this, we are profoundly grateful. The authors would like to apologise for any inconvenience caused.

AB - The authors would like to extend their heartfelt thanks to Dr. Chi Zhang of the Department of Meteorology and Geophysics at the University of Vienna, Austria, and Dr. Reza Barati of the Department of Chemical & Petroleum Engineering at the University of Kansas, USA. Their insightful discussions and ideas were vital in shaping the initial direction of our research. Their expertise and innovative perspectives have added substantial value to our work, and for this, we are profoundly grateful. The authors would like to apologise for any inconvenience caused.

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U2 - 10.1016/j.geoen.2023.212514

DO - 10.1016/j.geoen.2023.212514

M3 - Comment/debate

AN - SCOPUS:85187974763

SN - 2949-8910

VL - 237

JO - Geoenergy Science and Engineering

JF - Geoenergy Science and Engineering

M1 - 212514

ER -

Liu S , Fan M , Lu D. Corrigendum to “Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images” [Geoenergy Sci. Eng. (2023), Vol 230, 212160] (S2949891023007479), (10.1016/j.geoen.2023.212160). Geoenergy Science and Engineering. 2024 Jun;237:212514. doi: 10.1016/j.geoen.2023.212514

Corrigendum to “Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images” [Geoenergy Sci. Eng. (2023), Vol 230, 212160] (S2949891023007479), (10.1016/j.geoen.2023.212160)

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