Estimating fluid flow rates through fracture networks using combinatorial optimization

Hobé, Alex; Vogler, Daniel; Seybold, Martin P.; Ebigbo, Anozie; Settgast, Randolph R.; Saar, Martin O.

doi:10.1016/j.advwatres.2018.10.002

Cited by 17 publications

(15 citation statements)

References 69 publications

(91 reference statements)

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“…We focus on reporting problems for instances with fewer, k ∈ o(n 2 ), intersections. Creating intersection graphs is a natural precursor in meshing fracture networks [15]. Simple examples of such networks are sets of plane, simple polygons in R 3 with bounded complexity (e.g.…”

Section: Motivationmentioning

confidence: 99%

A Simple Dynamization of Trapezoidal Point Location in Planar Subdivisions

Brankovic¹,

Grujić²,

Renssen³

et al. 2019

Preprint

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We study how to dynamize the Trapezoidal Search Tree (TST) -a well known randomized point location structure for planar subdivisions of kinetic line segments.Our approach naturally extends incremental leaf-level insertions to recursive methods and allows adaptation for the online setting. Moreover, the dynamization carries over to the Trapezoidal Search DAG (TSD), offering a linear sized data structure with logarithmic point location costs as a by-product. On a set S of non-crossing segments, each update performs expected O(log 2 |S|) operations.We demonstrate the practicality of our method with an open-source implementation, based on the Computational Geometry Algorithms Library, and experiments on the update performance.

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Section: Motivationmentioning

confidence: 99%

A Simple Dynamization of Trapezoidal Point Location in Planar Subdivisions

Brankovic¹,

Grujić²,

Renssen³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Combinatorial optimization problems on graphs are ubiquitous in fields of science and engineering, such as bioinformatics [1,2], earth science [3], logistics [4], resource management [5], telecommunications [6], ecommerce [7,8] and others. Efficient classical algorithms for solving many of these problems are not known, and quantum computers can potentially provide an advantage.…”

Section: Introductionmentioning

confidence: 99%

Multi-round QAOA and advanced mixers on a trapped-ion quantum computer

Zhu¹,

Zhang²,

Sundar³

et al. 2022

Preprint

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“…As a consequence, the elusive link between medium structure attributes and complex flow distribution presents a challenge for accurate predictions of field-scale flow and transport problems. Relatable connectivity measures for static and dynamic metrics have been proposed to elucidate this link (Knudby and Carrera 2005;Renard and Allard 2013;Hobé et al 2018;Hyman 2020;Rizzo and de Barros 2017). Static connectivity measures are defined from structural information, while dynamic connectivity measures reflect the system's response in its flow field and/or solute transport.…”

Section: Introductionmentioning

confidence: 99%

Flow Path Resistance in Heterogeneous Porous Media Recast into a Graph-Theory Problem

et al. 2021

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This work aims to describe the spatial distribution of flow from characteristics of the underlying pore structure in heterogeneous porous media. Thousands of two-dimensional samples of polydispersed granular media are used to (1) obtain the velocity field via direct numerical simulations, and (2) conceptualize the pore network as a graph in each sample. Analysis of the flow field allows us to distinguish preferential from stagnant flow regions and to quantify how channelized the flow is. Then, the graph’s edges are weighted by geometric attributes of their corresponding pores to find the path of minimum resistance of each sample. Overlap between the preferential flow paths and the predicted minimum resistance path determines the accuracy in individual samples. An evolutionary algorithm is employed to determine the “fittest” weighting scheme (here, the channel’s arc length to pore throat ratio) that maximizes accuracy across the entire dataset while minimizing over-parameterization. Finally, the structural similarity of neighboring edges is analyzed to explain the spatial arrangement of preferential flow within the pore network. We find that connected edges within the preferential flow subnetwork are highly similar, while those within the stagnant flow subnetwork are dissimilar. The contrast in similarity between these regions increases with flow channelization, explaining the structural constraints to local flow. The proposed framework may be used for fast characterization of porous media heterogeneity relative to computationally expensive direct numerical simulations. Article Highlights A quantitative assessment of flow channeling is proposed that distinguishes pore-scale flow fields into preferential and stagnant flow regions. Geometry and topology of the pore network are used to predict the spatial distribution of fast flow paths from structural data alone. Local disorder of pore networks provides structural constraints for flow separation into preferential v stagnant regions and informs on their velocity contrast.

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Estimating fluid flow rates through fracture networks using combinatorial optimization

Cited by 17 publications

References 69 publications

A Simple Dynamization of Trapezoidal Point Location in Planar Subdivisions

A Simple Dynamization of Trapezoidal Point Location in Planar Subdivisions

Multi-round QAOA and advanced mixers on a trapped-ion quantum computer

Flow Path Resistance in Heterogeneous Porous Media Recast into a Graph-Theory Problem

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