Articles | Volume 10, issue 2
Research article
17 Apr 2019
Research article |  | 17 Apr 2019

A new methodology to train fracture network simulation using multiple-point statistics

Pierre-Olivier Bruna, Julien Straubhaar, Rahul Prabhakaran, Giovanni Bertotti, Kevin Bisdom, Grégoire Mariethoz, and Marco Meda


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
AR by Pierre-Olivier Bruna on behalf of the Authors (05 Feb 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (06 Feb 2019) by Federico Rossetti
RR by John Hooker (12 Mar 2019)
RR by William Dershowitz (25 Mar 2019)
ED: Publish subject to minor revisions (review by editor) (26 Mar 2019) by Federico Rossetti
AR by Anna Mirena Feist-Polner on behalf of the Authors (28 Mar 2019)  Author's response
ED: Publish as is (28 Mar 2019) by Federico Rossetti
ED: Publish as is (28 Mar 2019) by Federico Rossetti(Executive Editor)
Short summary
Natural fractures influence fluid flow in subsurface reservoirs. Our research presents a new methodology to predict the arrangement of these fractures in rocks. Contrary to the commonly used statistical models, our approach integrates more geology into the simulation process. The method is simply based on the drawing of images, can be applied to any type of rocks in various geological contexts, and is suited for fracture network prediction in water, geothermal, or hydrocarbon reservoirs.