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Articles | Volume 10, issue 2
Articles | Volume 10, issue 2
https://doi.org/10.5194/se-10-537-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-10-537-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

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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.
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