# Predicting absolute permeability#

The example explains absolute permeabilty calculations on a cubic network. Note that permeability calcualtion for an extracted network from PoreSpy follows similar steps in assigning phase, algorithm and calculating permeability.

import numpy as np
import openpnm as op
%config InlineBackend.figure_formats = ['svg']
np.random.seed(10)
%matplotlib inline
np.set_printoptions(precision=5)


## Create a random cubic network#

pn = op.network.Cubic(shape=[15, 15, 15], spacing=1e-6)
pn.regenerate_models()


## Create phase object#

It is assumed that a generic phase flowsthrough the porous medium. As absolute permeability is the porous medium property and not the fluid property, any other fluid with an assigned viscosity value can be used as the phase.

Permeability of the network is then found by applying Stokes Flow algorithm. To simulate the fluid flow algorithm, hydraulic conductance of the conduits must be defined. Here, the model collection is used to assign basic pore-scale models including generic_hydraulic to the phase.

phase = op.phase.Phase(network=pn)
phase['pore.viscosity']=1.0
phase.regenerate_models()

------------------------------------------------------------
- WARNING: throat.entry_pressure was not run since the
following property is missing:
'throat.surface_tension'
- SOURCE : openpnm.core._models.regenerate_models
- TIME   : 2022-09-21 01:41:59,564
------------------------------------------------------------

------------------------------------------------------------
- WARNING: throat.diffusive_conductance was not run since
the following property is missing:
'throat.diffusivity'
- SOURCE : openpnm.core._models.regenerate_models
- TIME   : 2022-09-21 01:41:59,587
------------------------------------------------------------


## Apply Stokes flow#

To calculate permeability in x direction, a constant pressure boundary condition is applied on the left and right side of the network. Note that a similar procedure can be followed to find the permeability in y and z directions.

inlet = pn.pores('left')
outlet = pn.pores('right')
flow = op.algorithms.StokesFlow(network=pn, phase=phase)
flow.set_value_BC(pores=inlet, values=1)
flow.set_value_BC(pores=outlet, values=0)
flow.run()
phase.update(flow.soln)

stokes_01 : Newton iterations:   0%|          | 0/100 [00:00<?, ?it/s]






Note

The Solution attribute

flow.soln is a dict with the quantity as the key (i.e. 'pore.pressure') and the solution as the value (i.e an ndarray). The last line in the cell above updates the phase with the new computed values of pore.pressure from solving the Stokes flow transport algorithm.


We can visulalize the pressure in the network:

ax = op.visualization.plot_connections(pn)
ax = op.visualization.plot_coordinates(pn, ax=ax, color_by=phase['pore.pressure'])


## Calculate permeability#

Calculate the permeability using Darcy’s law:

$K_{abs}= \frac{Q}{A} \frac{\mu L} {\Delta P}$

where $$Q$$ is the inlet flow rate, $$A$$ is the inlet area, and $$L$$ is the distance between inlet and outlet. As pressure difference and viscosity were assumed to be 1, we have a simplified equation.

# NBVAL_IGNORE_OUTPUT
Q = flow.rate(pores=inlet, mode='group')[0]
A = op.topotools.get_domain_area(pn, inlets=inlet, outlets=outlet)
L = op.topotools.get_domain_length(pn, inlets=inlet, outlets=outlet)
# K = Q * L * mu / (A * Delta_P) # mu and Delta_P were assumed to be 1.
K = Q * L / A
print(f'The value of K is: {K/0.98e-12*1000:.2f} mD')

------------------------------------------------------------
- WARNING: Attempting to estimate inlet area...will be low
- SOURCE : openpnm.topotools._topotools.get_domain_area
- TIME   : 2022-09-21 01:42:01,793
------------------------------------------------------------

------------------------------------------------------------
- WARNING: Attempting to estimate domain length...could be
low if boundary pores were not added
- SOURCE : openpnm.topotools._topotools.get_domain_length
- TIME   : 2022-09-21 01:42:01,795
------------------------------------------------------------

The value of K is: 0.07 mD


1) The methods of finding domain area in topotools is based on Scipy's ConvexHull, where a convexhull that includes the inlet pores will be created to approximate the inlet area. Both get_domain_area and get_domain_length can be a useful approximation in estimating area and length in extracted networks. In extracted networks without boundary pores where inlet/outlet pores are not necessarily located on an almost flat plane, the estimated value could be low.

2) In this example we assumed the network has spherical pores and cylindrical throats. Different geometrical shapes for pores and throats are defined in geometry collection. Each geometry collection includes pore-scale size factor models that are necessary for finding hydraulic conductance of the conduits and applying transport algorithm, accordingly. These size factor models can alternatively be assigned using add_model method to the network and choosing the model from op.models.geometry.hydraulic_size_factors. For more information on these pore-scale models see size factor example notebook.