Preprints
https://doi.org/10.5194/egusphere-2023-1316
https://doi.org/10.5194/egusphere-2023-1316
22 Jun 2023
 | 22 Jun 2023

A new tool to automatise the characterisation of fracture networks from 3D point cloud data

Lionel Bertrand, Claire Bossennec, Wan-Chiu Li, Cédric Borgese, Bruno Gavazzi, Matthis Frey, Yves Géraud, Marc Diraison, and Ingo Sass

Abstract. Fracture networks linked to the brittle deformation of rocks are often hosts of fluid flows or geomechanical discontinuities which are important to model for rock mass stability analyses, reservoir assessment or storage capacity evaluation. However, the fractures can be mapped only poorly by subsurface geophysics or borehole imagery. 1D scan lines or 2D maps on outcrops analogues are thus one of the current methods used for the assessemet of the networks, but are very limited regarding the 3D distribution of the fractures. This paper shows the first tests and results for a new automated workflow of fracture properties characterization, using high resolution LiDAR and Photogrammetry data on outcrops. From the obtained 3d point clouds on different objects such as quarries, cliffs or road sides, we reconstruct the outcrop surface in a 3D meshed surface with the help of a Simple and Scalable Surface Reconstruction (SSSR). The fracture planes are automatically detected based on a region-growing segmentation approach based on the spatial distribution of the points. It allows to quickly and effectively extract fracture properties such as orientation, geometry and position in the 3d space, with a possibility for direct 3D density computation without using proxies from 1D or 2D data. This paper present the workflow methodology and the tests on 5 basement rock outcrops of different outcropping quality. We also compare the method with a manual fracture picking tool and a classical 1D scan line methodology on the field. It is a first step to get more precise, automatic, easier and faster modelling workflows on fractured rocks from outcrop analogues data.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Lionel Bertrand, Claire Bossennec, Wan-Chiu Li, Cédric Borgese, Bruno Gavazzi, Matthis Frey, Yves Géraud, Marc Diraison, and Ingo Sass

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1316', Adam Cawood, 08 Jul 2023
    • AC1: 'Reply on RC1', Lionel Bertrand, 02 Oct 2023
  • RC2: 'Comment on egusphere-2023-1316', Thomas Seers, 05 Sep 2023
    • AC2: 'Reply on RC2', Lionel Bertrand, 02 Oct 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1316', Adam Cawood, 08 Jul 2023
    • AC1: 'Reply on RC1', Lionel Bertrand, 02 Oct 2023
  • RC2: 'Comment on egusphere-2023-1316', Thomas Seers, 05 Sep 2023
    • AC2: 'Reply on RC2', Lionel Bertrand, 02 Oct 2023
Lionel Bertrand, Claire Bossennec, Wan-Chiu Li, Cédric Borgese, Bruno Gavazzi, Matthis Frey, Yves Géraud, Marc Diraison, and Ingo Sass
Lionel Bertrand, Claire Bossennec, Wan-Chiu Li, Cédric Borgese, Bruno Gavazzi, Matthis Frey, Yves Géraud, Marc Diraison, and Ingo Sass

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Short summary
The assessement of fracture networks is a key element for underground reservoir studies. The available methods for such assessement are unfortunately very limited in the case of complex 3 dimensions geometries. The paper shows a new method to overcome these limitations through automatic detection from images of outcrops.