import numpy as np
from edt import edt
from porespy.filters import trim_disconnected_blobs, find_trapped_regions
from porespy.filters import pc_to_satn, satn_to_seq
from porespy import settings
from porespy.tools import _insert_disks_at_points
from porespy.tools import get_tqdm
from porespy.tools import Results
from porespy.metrics import pc_curve
tqdm = get_tqdm()
__all__ = [
'drainage',
]
[docs]
def drainage(im, voxel_size, pc=None, inlets=None, outlets=None, residual=None,
bins=25, delta_rho=1000, g=9.81, sigma=0.072, theta=180):
r"""
Simulate drainage using image-based sphere insertion, optionally including
gravity
Parameters
----------
im : ndarray
The image of the porous media with ``True`` values indicating the
void space.
voxel_size : float
The resolution of the image in meters per voxel edge.
pc : ndarray, optional
An array containing precomputed capillary pressure values in each
voxel. If not provided then the Washburn equation is used with the
provided values of ``sigma`` and ``theta``. If the image is 2D only
1 principle radii of curvature is included.
inlets : ndarray (default = x0)
A boolean image the same shape as ``im``, with ``True`` values
indicating the inlet locations. See Notes. If not specified it is
assumed that the invading phase enters from the bottom (x=0).
outlets : ndarray, optional
Similar to ``inlets`` except defining the outlets. This image is used
to assess trapping. \If not provided then trapping is ignored,
otherwise a mask indicating which voxels were trapped is included
amoung the returned data.
residual : ndarray, optional
A boolean array indicating the locations of any residual defending
phase. This is added to the intermediate image prior to trimming
disconnected clusters, so will create connections to some clusters
that would otherwise be removed. The residual phase is indicated
in the final image by ``-np.inf`` values, since there are invaded at
all applied capillary pressures.
bins : int or array_like (default = 25)
The range of pressures to apply. If an integer is given
then bins will be created between the lowest and highest pressures
in the ``pc``. If a list is given, each value in the list is used
directly in order.
delta_rho : float (default = 997)
The density difference between the invading and defending phases.
Note that if air is displacing water this value should be -997 (1-998).
g : float (default = 9.81)
The gravitational constant prevailing for the simulation. The default
is 9.81. If the domain is on an angle, such as a tilted micromodel,
this value should be scaled appropriately by the user
(i.e. g = 9.81 sin(alpha) where alpha is the angle relative to the
horizonal). Setting this value to zeor removes any gravity effects.
sigma : float (default = 0.072)
The surface tension of the fluid pair. If ``pc`` is provided this is
ignored.
theta : float (defaut = 180)
The contact angle of the sytem in degrees. If ``pc`` is provded this
is ignored.
Returns
-------
results : Results object
A dataclass-like object with the following attributes:
========== ============================================================
Attribute Description
========== ============================================================
im_pc A numpy array with each voxel value indicating the
capillary pressure at which it was invaded
im_satn A numpy array with each voxel value indicating the global
saturation value at the point it was invaded
pc 1D array of capillary pressure values that were applied
swnp 1D array of non-wetting phase saturations for each applied
value of capillary pressure (``pc``).
========== ============================================================
Notes
-----
- The direction of gravity is always towards the x=0 axis
- This algorithm has only been tested for gravity stabilized
configurations, meaning the more dense fluid is on the bottom.
Be sure that ``inlets`` are specified accordingly.
Examples
--------
`Click here
<https://porespy.org/examples/simulations/reference/drainage.html>`_
to view online example.
"""
im = np.array(im, dtype=bool)
dt = edt(im)
if pc is None:
pc = -(im.ndim-1)*sigma*np.cos(np.deg2rad(theta))/(dt*voxel_size)
pc[~im] = 0 # Remove any infs or nans from pc computation
# Generate image for correcting entry pressure by gravitational effects
h = np.ones_like(im, dtype=bool)
h[0, ...] = False
h = (edt(h) + 1)*voxel_size # This could be done quicker using clever logic
rgh = delta_rho*g*h
fn = pc + rgh
if inlets is None:
inlets = np.zeros_like(im)
inlets[0, ...] = True
if isinstance(bins, int): # Use values fn for invasion steps
vmax = fn[fn < np.inf].max()
vmin = fn[im][fn[im] > -np.inf].min()
Ps = np.linspace(vmin, vmax*1.1, bins)
else:
Ps = bins
# Initialize empty arrays to accumulate results of each loop
inv = np.zeros_like(im, dtype=float)
seeds = np.zeros_like(im, dtype=bool)
# Deal with any void space trapped behind residual blobs
mask = None
if (residual is not None) and (outlets is not None):
mask = im * (~residual)
mask = trim_disconnected_blobs(mask, inlets=inlets)
for p in tqdm(Ps, **settings.tqdm):
# Find all locations in image invadable at current pressure
temp = (fn <= p)*im
# Add residual so that fluid is more easily reconnected
if residual is not None:
temp = temp + residual
# Trim locations not connected to the inlets
new_seeds = trim_disconnected_blobs(temp, inlets=inlets)
# Trim locations not connected to the outlet
if mask is not None:
new_seeds = new_seeds * mask
# Isolate only newly found locations to speed up inserting
temp = new_seeds*(~seeds)
# Find i,j,k coordinates of new locations
coords = np.where(temp)
# Add new locations to list of invaded locations
seeds += new_seeds
# Extract the local size of sphere to insert at each new location
radii = dt[coords].astype(int)
# Insert spheres are new locations of given radii
inv = _insert_disks_at_points(im=inv, coords=np.vstack(coords),
radii=radii, v=p, smooth=True)
# Set uninvaded voxels to inf
inv[(inv == 0)*im] = np.inf
# Add residual if given
if residual is not None:
inv[residual] = -np.inf
# Initialize results object
results = Results()
trapped = None
satn = pc_to_satn(pc=inv, im=im)
if outlets is not None:
seq = satn_to_seq(satn=satn, im=im)
trapped = find_trapped_regions(seq=seq, outlets=outlets)
trapped[seq == -1] = True
inv[trapped] = np.inf
if residual is not None: # Re-add residual to inv
inv[residual] = -np.inf
satn = pc_to_satn(pc=inv, im=im)
results.im_satn = satn
results.im_pc = inv
results.im_trapped = trapped
_pccurve = pc_curve(im=im, pc=inv)
results.pc = _pccurve.pc
results.snwp = _pccurve.snwp
return results
if __name__ == "__main__":
import numpy as np
import porespy as ps
import matplotlib.pyplot as plt
from copy import copy
# %% Run this cell to regenerate the variables in drainage
np.random.seed(6)
bg = 'white'
plots = True
im = ps.generators.blobs(shape=[500, 500], porosity=0.7, blobiness=1.5)
inlets = np.zeros_like(im)
inlets[0, :] = True
outlets = np.zeros_like(im)
outlets[-1, :] = True
pc = None
lt = ps.filters.local_thickness(im)
residual = lt > 25
bins = 25
voxel_size = 1e-4
sigma = 0.072
theta = 180
delta_rho = 1000
g = 9.81
# %% Run 4 different drainage simulations
drn1 = ps.simulations.drainage(im=im, voxel_size=voxel_size,
inlets=inlets,
delta_rho=delta_rho,
g=g)
drn2 = ps.simulations.drainage(im=im, voxel_size=voxel_size,
inlets=inlets, outlets=outlets,
delta_rho=delta_rho,
g=g)
drn3 = ps.simulations.drainage(im=im, voxel_size=voxel_size,
inlets=inlets,
residual=residual,
delta_rho=delta_rho,
g=g)
drn4 = ps.simulations.drainage(im=im, voxel_size=voxel_size,
inlets=inlets, outlets=outlets,
residual=residual,
delta_rho=delta_rho,
g=g)
# %% Visualize the invasion configurations for each scenario
if plots:
cmap = copy(plt.cm.plasma)
cmap.set_under(color='black')
cmap.set_over(color='grey')
fig, ax = plt.subplots(2, 2, facecolor=bg)
kw = ps.visualization.prep_for_imshow(drn1.im_pc, im)
ax[0][0].imshow(**kw, cmap=cmap)
ax[0][0].set_title("No trapping, no residual")
kw = ps.visualization.prep_for_imshow(drn2.im_pc, im)
ax[0][1].imshow(**kw, cmap=cmap)
ax[0][1].set_title("With trapping, no residual")
kw = ps.visualization.prep_for_imshow(drn3.im_pc, im)
ax[1][0].imshow(**kw, cmap=cmap)
ax[1][0].set_title("No trapping, with residual")
kw = ps.visualization. prep_for_imshow(drn4.im_pc, im)
ax[1][1].imshow(**kw, cmap=cmap)
ax[1][1].set_title("With trapping, with residual")
# %% Plot the capillary pressure curves for each scenario
if plots:
plt.figure(facecolor=bg)
ax = plt.axes()
ax.set_facecolor(bg)
plt.step(drn1.pc, drn1.snwp, 'b-o', where='post',
label="No trapping, no residual")
plt.step(drn2.pc, drn2.snwp, 'r--o', where='post',
label="With trapping, no residual")
plt.step(drn3.pc, drn3.snwp, 'g--o', where='post',
label="No trapping, with residual")
plt.step(drn4.pc, drn4.snwp, 'm--o', where='post',
label="With trapping, with residual")
plt.legend()