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Estimating fluid flow rates through fracture networks using combinatorial optimization
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Geovetenskapliga sektionen, Institutionen för geovetenskaper, Geofysik. Swiss Fed Inst Technol, Geothermal Energy & Geofluids, Inst Geophys, Zurich, Switzerland.
Swiss Fed Inst Technol, Geothermal Energy & Geofluids, Inst Geophys, Zurich, Switzerland;Swiss Fed Inst Technol, Transport Proc & React Lab, Inst Proc Engn, Zurich, Switzerland.
Univ Stuttgart, Inst Formal Methods Comp Sci, Stuttgart, Germany.
Swiss Fed Inst Technol, Geothermal Energy & Geofluids, Inst Geophys, Zurich, Switzerland.
Vise andre og tillknytning
2018 (engelsk)Inngår i: Advances in Water Resources, ISSN 0309-1708, E-ISSN 1872-9657, Vol. 122, s. 85-97Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

To enable fast uncertainty quantification of fluid flow in a discrete fracture network (DFN), we present two approaches to quickly compute fluid flow in DFNs using combinatorial optimization algorithms. Specifically, the presented Hanan Shortest Path Maxflow (HSPM) and Intersection Shortest Path Maxflow (ISPM) methods translate DFN geometries and properties to a graph on which a max flow algorithm computes a combinatorial flow, from which an overall fluid flow rate is estimated using a shortest path decomposition of this flow. The two approaches are assessed by comparing their predictions with results from explicit numerical simulations of simple test cases as well as stochastic DFN realizations covering a range of fracture densities. Both methods have a high accuracy and very low computational cost, which can facilitate much-needed in-depth analyses of the propagation of uncertainty in fracture and fracture-network properties to fluid flow rates.

sted, utgiver, år, opplag, sider
2018. Vol. 122, s. 85-97
Emneord [en]
Discrete fracture networks, Fluid flow, Permeability, Combinatorial optimization, Numerical methods
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-371042DOI: 10.1016/j.advwatres.2018.10.002ISI: 000450094200007OAI: oai:DiVA.org:uu-371042DiVA, id: diva2:1275538
Tilgjengelig fra: 2019-01-07 Laget: 2019-01-07 Sist oppdatert: 2019-01-07bibliografisk kontrollert

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