The hidden structure of hydrodynamic transport in random fracture networks

Journal Article · Thu Dec 21 00:00:00 EST 2023 · Journal of Fluid Mechanics

DOI:https://doi.org/10.1017/jfm.2023.973· OSTI ID:2406559

Dentz, Marco ^[1]; Hyman, Jeffrey D. ^[2]

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1. Consejo Superior de Investigaciones Cientificas (CSIC), Barcelona (Spain)
2. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

We study the large-scale dynamics and prediction of hydrodynamic transport in random fracture networks. The flow and transport behaviour is characterized by first passage times and displacement statistics, which show heavy tails and anomalous dispersion with a strong dependence on the injection condition. The origin of these behaviours is investigated in terms of Lagrangian velocities sampled equidistantly along particle trajectories, unlike classical sampling strategies at a constant rate. The velocity series are analysed by their copula density, the joint distribution of the velocity unit scores, which reveals a simple, albeit hidden, correlation structure that can be described by a Gaussian copula. Based on this insight, we derive a Langevin equation for the evolution of equidistant particle speeds. In this framework, particle motion is quantified by a stochastic time-domain random walk, the joint density of particle position, and speed satisfies a Klein–Kramers equation. The upscaled theory quantifies particle motion in terms of the characteristic fracture length scale and the distribution of Eulerian flow velocities. That is, it is predictive in the sense that it does not require the a priori knowledge of transport attributes. The upscaled model captures non-Fickian transport features, and their dependence on the injection conditions in terms of the velocity point statistics and average fracture length. It shows that the first passage times and displacement moments are dominated by extremes occurring at the first step. The presented approach integrates the interaction of flow and structure into a predictive model for large-scale transport in random fracture networks.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: European Research Council (ERC); USDOE Laboratory Directed Research and Development (LDRD) Program
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 2406559
• Report Number(s): LA-UR--23-20055
• Journal Information: Journal of Fluid Mechanics, Journal Name: Journal of Fluid Mechanics Vol. 977; ISSN 0022-1120
• Publisher: Cambridge University Press
• Country of Publication: United States
• Language: English

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