Comparison of the response to geometrical complexity of methods for unstationary simulations in discrete fracture networks with conforming, polygonal, and non-matching grids

Abstract The aim of this study is to compare numerical methods for the simulation of single-phase flow and transport in fractured media, described here by means of the discrete fracture network (DFN) model. A Darcy problem is solved to compute the advective field, then used in a subsequent time-dependent transport-diffusion-reaction problem. The numerical schemes are benchmarked in terms of flexibility in handling geometrical complexity, mass conservation, and stability issues for advection-dominated flow regimes. To this end, two benchmark cases, along with an additional one from a previous work, have been specifically designed and are here proposed and investigated, representing some of the most critical issues encountered in DFN simulations.

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Particle-Based Workflow for Modeling Uncertainty of Reactive Transport in 3D Discrete Fracture Networks

Water ◽

10.3390/w11122502 ◽

2019 ◽

Vol 11 (12) ◽

pp. 2502 ◽

Cited By ~ 1

Author(s):

Phuong Thanh Vu ◽

Chuen-Fa Ni ◽

Wei-Ci Li ◽

I-Hsien Lee ◽

Chi-Ping Lin

Keyword(s):

Fractured Rocks ◽

Reactive Transport ◽

Fracture Network ◽

Reaction Model ◽

Transport Processes ◽

Fracture Networks ◽

Flow Paths ◽

Complex Fracture ◽

Discrete Fracture Networks ◽

Discrete Fracture

Fractures are major flow paths for solute transport in fractured rocks. Conducting numerical simulations of reactive transport in fractured rocks is a challenging task because of complex fracture connections and the associated nonuniform flows and chemical reactions. The study presents a computational workflow that can approximately simulate flow and reactive transport in complex fractured media. The workflow involves a series of computational processes. Specifically, the workflow employs a simple particle tracking (PT) algorithm to track flow paths in complex 3D discrete fracture networks (DFNs). The PHREEQC chemical reaction model is then used to simulate the reactive transport along particle traces. The study illustrates the developed workflow with three numerical examples, including a case with a simple fracture connection and two cases with a complex fracture network system. Results show that the integration processes in the workflow successfully model the tetrachloroethylene (PCE) and trichloroethylene (TCE) degradation and transport along particle traces in complex DFNs. The statistics of concentration along particle traces enables the estimations of uncertainty induced by the fracture structures in DFNs. The types of source contaminants can lead to slight variations of particle traces and influence the long term reactive transport. The concentration uncertainty can propagate from parent to daughter compounds and accumulate along with the transport processes.

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Determination of Volumetric Joint Count Based on 3D Fracture Network and its Application in Engineering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.580-583.907 ◽

2014 ◽

Vol 580-583 ◽

pp. 907-911

Author(s):

Jin Liang Wu ◽

Ji He

Keyword(s):

Indirect Method ◽

Fracture Network ◽

Rock Masses ◽

Fracture Spacing ◽

The Monte Carlo Method ◽

Discrete Fracture Networks ◽

Discrete Fracture ◽

Survey Line

Volumetric joint countis an important parameter to evaluate the development of fractures. It is a fundamental representative for the strength and permeability of rock masses. However, cannot be directly measured in field. In this study, an indirect method is applied for its estimation. The main procedures are as follows: firstly, the volumetric joint frequencyis assumed for th fracture set, and then a series of 3D stochastic discrete fracture networks (DFNs) are generated using the Monte Carlo method according to; secondly, a survey line is drawn perpendicular to the fracture set in the each fracture network generated, the fracture spacing is measured along the survey line, then the average fracture spacing and its variance coefficient are calculated from all the DFNs; thirdly, by repeating the above two steps for differentassumed, the relevant average fracture spacing and its variance coefficient are obtained, and two relation curves are built up between and the average fracture spacing (or its variance coefficient); fourth, the exactis estimated through this relation curve between and the average fracture spacing according to the exact fracture spacing measured in situ; finally,is calculated by summing the exactof all fracture sets up. In this study, this indirect method is applied in the rock masses of Xiaowan Hydropower Station. The result shows that the fracture spacing will reduce and its variation coefficient becomes stable asincreases.

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Machine learning for flux regression in discrete fracture networks

GEM - International Journal on Geomathematics ◽

10.1007/s13137-021-00176-0 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

S. Berrone ◽

F. Della Santa ◽

S. Pieraccini ◽

F. Vaccarino

Keyword(s):

Fractured Rock ◽

Fracture Network ◽

Flow Simulations ◽

Reduction Techniques ◽

Discrete Fracture Networks ◽

Stochastic Parameters ◽

Discrete Fracture ◽

Flow Through ◽

Regression Problems ◽

Underground Flow

AbstractIn several applications concerning underground flow simulations in fractured media, the fractured rock matrix is modeled by means of the Discrete Fracture Network (DFN) model. The fractures are typically described through stochastic parameters sampled from known distributions. In this framework, it is worth considering the application of suitable complexity reduction techniques, also in view of possible uncertainty quantification analyses or other applications requiring a fast approximation of the flow through the network. Herein, we propose the application of Neural Networks to flux regression problems in a DFN characterized by stochastic trasmissivities as an approach to predict fluxes.

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