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Scaling of fractured rock flow. Proposition of indicators for selection of DFN based flow models
Univ Rennes, CNRS, Geosciences Rennes, UMR 6118, 263 Avenue General Leclerc, Rennes 35042, France.
Itasca Consultants S.A.S., 29 Avenue Joannes Masset, Lyon 69009, France.
Itasca Consultants S.A.S., 29 Avenue Joannes Masset, Lyon 69009, France.
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering. Swedish Nuclear Fuel and Waste Management Company (SKB), Evenemangsgatan 13, Box 3091, Solna SE-169 03, Sweden.ORCID iD: 0000-0001-9251-5367
2023 (English)In: Comptes rendus Geoscience, ISSN 1631-0713, E-ISSN 1778-7025, Vol. 355Article in journal (Refereed) Published
Abstract [en]

The objective of the paper is to better understand and quantify the flow structure in fractured rocks from flow logs, and to propose relevant indicators for validating, calibrating or even rejecting hydrogeological models. We first studied what the inflow distribution tells us about the permeability structure from a series of analyses: distribution of transmissivities as a function of depth, proportion of flowing sections as a function of section scale, and scaling of the arithmetically-averaged and geometrically-averaged permeability. We then define three indicators that describe few fundamental characteristics of the flow/permeability, whatever the scale: a percolation scale ls, the way permeability increases with scale above ls, and the variability of permeability. A 4th indicator on the representative elemental volume could in principle be defined but the data show that this volume/scale is beyond the 300 m investigated. We tested a series of numerical models built in three steps: the geo-DFN based on the observed fracture network, the open-DFN which is the part of the geo-DFN where fractures are open, and a transmissivity model applying on each fracture of the open-DFN (Discrete Fracture Network). The analysis of the models showed that the percolation scale is controlled by the open-DFN structure and that the percolation scale can be predicted from a scale analysis of the percolation parameter (basically, the third moment of the fracture size distribution that provides a measure of the network connectivity). The way permeability increases with scale above the percolation threshold is controlled by the transmissivity model and in particular by the dependence of the fracture transmissivity on either the orientation of the fractures via a stress-controlled transmissivity or their size or both. The comparison with data on the first two indicators shows that a model that matches the characteristics of the geo-DFN with an open fraction of 15% as measured adequately fits the data provided that the large fractures remain open and that the fracture transmissivity model is well selected. Most of the other models show unacceptable differences with data but other models or model combinations has still to be explored beforerejecting them. The third indicator on model variability is still problematic since the natural data show a higher variability than the models but the open fraction is also much more variable in the data than in the models.

Place, publisher, year, edition, pages
Cellule MathDoc/CEDRAM , 2023. Vol. 355
Keywords [en]
Crystalline rocks, Fracture network, Indicator, Percolation, Permeability, Scaling
National Category
Oceanography, Hydrology and Water Resources
Identifiers
URN: urn:nbn:se:kth:diva-331605DOI: 10.5802/crgeos.174Scopus ID: 2-s2.0-85153936693OAI: oai:DiVA.org:kth-331605DiVA, id: diva2:1781971
Note

QC 20230711

Available from: 2023-07-11 Created: 2023-07-11 Last updated: 2023-09-06Bibliographically approved

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Selroos, Jan-Olof

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