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Response of a gravity current in a porous medium to a perturbation of the permeability field

Published online by Cambridge University Press:  02 December 2025

Emily Flicos*
Affiliation:
Department of Earth Sciences, University of Cambridge , Cambridge CB3 0EZ, UK Institute for Energy and Environmental Flows, University of Cambridge , Cambridge CB3 0EZ, UK
Jerome Neufeld
Affiliation:
Department of Earth Sciences, University of Cambridge , Cambridge CB3 0EZ, UK Institute for Energy and Environmental Flows, University of Cambridge , Cambridge CB3 0EZ, UK Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, UK
*
Corresponding author: Emily Flicos, ejf61@cam.ac.uk

Abstract

One of the challenges with modelling subsurface flows is the uncertainty in measurements of geological properties, mostly due to limited resolution in observation methods. Many subsurface flows can be modelled as a gravity current, which, for uniform material properties and power-law injection rate, has a well-characterised similarity solution. The similarity solution forms a dynamical attractor that is typically approached rapidly from a host of initial conditions. Here, we consider the impact of transverse variations to the permeability field by performing a perturbation analysis of the self-similar spreading. This treats the response as perturbations to the self-similar flow. We restrict our focus to permeability fields that vary laterally, or across the flow, starting with the simple case of a sinusoidal perturbation to a uniform permeability. At early times, the height and nose position of the current are determined by the local permeability, and deviations to the height and nose grow at the same rate as the mean, and proportional to the amplitude, of the permeability variation. The transition between the early and late time regimes is governed by the wavelength of the permeability. At late times, lateral spreading between high and low permeability streaks is dominant; the height deviations decay, and the nose deviations approach a steady state. The magnitudes of both depend on the product of the wavelength and amplitude of the permeability. The single mode sets the groundwork for examining more complex, multimodal permeabilities, which are more representative of real geological structures.

Information

Type
JFM Papers
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press
Figure 0

Figure 1. Schematic showing line injection at $x=0$ of buoyant fluid into unconfined ambient.

Figure 1

Figure 2. Simulation results for $\varepsilon =0.1$. The two columns show the same simulations at two different times: (a,c,e) $t=0.215$ and (b,d,f) $t=62.4$. Graphs show (a,b) the height, $h$, (c,d) the deviations from the transverse mean height, $h-\overline {h}$, and (e,f) three slices of the full height: the minimum (orange circles), transverse mean (magenta crosses) and maximum (green plusses). The minimum and maximum are taken from the corresponding orange and green lines in (a,b). An inset shows the deviation of the minimum and maximum from the mean. The black solid line is the self-similar solution (4.3b), which the simulation mean matches.

Figure 2

Figure 3. Height and height perturbations scaled by $t^{-1/3}$ in similarity space. Simulations shown with symbols and the theoretical result from solving (4.20) are shown with the black solid line. Heights in the simulation are evaluated at midpoints of grid cells, and the grid spacing at injection increases with time, which creates the small gap in simulation data towards $\zeta =0$ for $t=0.35$.

Figure 3

Figure 4. Evolution of nose perturbation for a range of $\varepsilon$ values. Symbols show simulations, calculated as $( \max {x_N} - \min {x_N} )/2$. Solid lines show theory of early behaviour (4.22) and late behaviour (4.43).

Figure 4

Figure 5. (a) Height deviations back from the mean nose position. Simulations are shown with symbols, theory for the nose (4.41) and interior (4.28) with solid lines, and the composite solution (4.42) with dashed lines. (b) Ratio of the expected prefactor from (4.27) to the one fit to simulations, $\alpha (t)$.

Figure 5

Figure 6. Evolution of (a,c) $x^{\prime}_N$ and (b,d) $x^{\prime\prime}_N$ with time for permeability perturbation amplitudes (a,b) $\varepsilon =0.1$ and (c,d) $\varepsilon =0.5$.

Figure 6

Figure 7. Amplitude of higher-order modes forced by single mode permeability field. Solid line shows linear theory. The amplitude of the permeability perturbations is (a) $\varepsilon =0.1$ and (b) $\varepsilon =0.5$.

Figure 7

Figure 8. Probability distributions from which the $\varepsilon _m$ values are taken alternately for the simulations.

Figure 8

Table 1. Permeability parameters for runs A and B, with $W=2$.

Figure 9

Figure 9. Run A. (a) The permeability profile used in this simulation. (b) The evolution of the deviations from the mean nose position from early to late times (only $0\lt y\lt 2$ is shown since permeability, and hence the nose, is symmetric about $y=0$). (c) The FFT of the nose profile, showing amplitude of contribution from each mode over time, with results from simulations shown with symbols, and theory with solid lines. Only modes imposed in the permeability variations are shown.

Figure 10

Figure 10. Run B. (a) The permeability profile used in this simulation. (b) The evolution of the deviations from the mean nose position from early to late times (only $0\lt y\lt 2$ is shown since permeability, and hence the nose, is symmetric about $y=0$). (c) The FFT of the nose profile, showing amplitude of contribution from each mode over time, with results from simulations shown with symbols, and theory with solid lines. Only modes imposed in the permeability variations are shown.

Figure 11

Figure 11. Ratio of observed $X_{m,{sim}}$ to expected final nose perturbation value $X_{m,{theory}}$ given by (5.11). Simulation value taken as average of final five points.

Figure 12

Table 2. Typical reservoir values from Benham et al. (2022).