Articles | Volume 22, issue 12
https://doi.org/10.5194/hess-22-6547-2018
https://doi.org/10.5194/hess-22-6547-2018
Research article
 | 
20 Dec 2018
Research article |  | 20 Dec 2018

Locality-based 3-D multiple-point statistics reconstruction using 2-D geological cross sections

Qiyu Chen, Gregoire Mariethoz, Gang Liu, Alessandro Comunian, and Xiaogang Ma

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (03 Oct 2018) by Philippe Ackerer
ED: Publish subject to revisions (further review by editor and referees) (04 Oct 2018) by Philippe Ackerer
AR by Gang Liu on behalf of the Authors (05 Oct 2018)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (10 Oct 2018) by Philippe Ackerer
RR by Thomas Hermans (16 Oct 2018)
RR by Kashif Mahmud (12 Nov 2018)
ED: Publish subject to minor revisions (review by editor) (22 Nov 2018) by Philippe Ackerer
AR by Gang Liu on behalf of the Authors (26 Nov 2018)  Author's response   Manuscript 
ED: Publish as is (06 Dec 2018) by Philippe Ackerer
AR by Gang Liu on behalf of the Authors (07 Dec 2018)
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Short summary
One of the critical issues in MPS simulation is the difficulty in obtaining a credible 3-D training image. We propose an MPS-based 3-D reconstruction method on the basis of 2-D cross sections, making 3-D training images unnecessary. The main advantages of this approach are the high computational efficiency and a relaxation of the stationarity assumption. The results, in comparison with previous MPS methods, show better performance in portraying anisotropy characteristics and in CPU cost.