satn_profile#

Computes the saturation profiles in an invasion image

import matplotlib.pyplot as plt
import numpy as np
import porespy as ps

[17:46:20] ERROR    PARDISO solver not installed, run `pip install pypardiso`. Otherwise,          _workspace.py:56
                    simulations will be slow. Apple M chips not supported.

The arguments and their defaults for this function are:

import inspect
inspect.signature(ps.metrics.satn_profile)

<Signature (satn, s=None, im=None, axis=0, span=10, mode='tile')>

Start by performing a basic invasion simulation:

np.random.seed(1)
im = ps.generators.blobs(shape=[150, 150], porosity=0.6, blobiness=1)
inlets = np.zeros_like(im)
inlets[0, :] = True
inv = ps.simulations.drainage(im=im, inlets=inlets, voxel_size=1, g=0)

fig, ax = plt.subplots(1, 2, figsize=[12, 6])
ax[0].imshow(inv.im_satn/im, interpolation='none', origin='lower')
ax[0].axis(False)
ax[0].set_title('Saturation map')
ax[1].imshow((inv.im_satn < 0.6)*(inv.im_satn > 0)/im, interpolation='none', origin='lower')
ax[1].axis(False)
ax[1].set_title('Fluid distribution at saturation = 0.6');
../../../_images/a3da79bd64c8e9a289aaea0a3179c69cdb65d90dafbc9a559968c91a782ecc59.png

satn#

This is the output of the invasion function, converted to saturation if needed:

s = ps.metrics.satn_profile(satn=inv.im_satn, s=0.6)
plt.plot(s.position, s.saturation, 'b-o')
plt.xlabel("distance from injection face")
plt.ylabel("non-wetting phase saturation");
../../../_images/6a1c957a504a3d299c2f46a8a1c22ffb491ef4cd74e18e625b2b557f5f5383dd.png

s#

The global saturation for which the profile should be obtained:

s = 0.6
s1 = ps.metrics.satn_profile(satn=inv.im_satn, s=s)
plt.plot(s1.position, s1.saturation, 'b-o')

s = 0.4
s2 = ps.metrics.satn_profile(satn=inv.im_satn, s=s)
plt.plot(s2.position, s2.saturation, 'r-o')

s = 0.1
s3 = ps.metrics.satn_profile(satn=inv.im_satn, s=s)
plt.plot(s3.position, s3.saturation, 'g-o')

plt.xlabel("distance from injection face")
plt.ylabel("non-wetting phase saturation");
../../../_images/760413c38097bf45b894048490e074258c603018028934ba6298660372162533.png

span#

The width of the slice over which the saturation is computed. The default is 10 voxels. A higher number makes the curve smoother, but risks losing features like dips and spikes:

s = 5
s1 = ps.metrics.satn_profile(satn=inv.im_satn, s=0.6, span=s)
plt.plot(s1.position, s1.saturation, 'b-o')

s = 10
s2 = ps.metrics.satn_profile(satn=inv.im_satn, s=0.6, span=s)
plt.plot(s2.position, s2.saturation, 'r-o')

s = 30
s3 = ps.metrics.satn_profile(satn=inv.im_satn, s=0.6, span=s)
plt.plot(s3.position, s3.saturation, 'g-o')

plt.xlabel("distance from injection face")
plt.ylabel("non-wetting phase saturation");
../../../_images/f9952337434ee980794c69bd3bc32705a0cfa97b389a90acd3c6331636ec9bb5.png

mode#

How the averaging window moves, either by sliding or by tiling.

s1 = ps.metrics.satn_profile(satn=inv.im_satn, s=0.6, mode='slide')
plt.plot(s1.position, s1.saturation, 'b-o')

s = 10
s2 = ps.metrics.satn_profile(satn=inv.im_satn, s=0.6, mode='tile')
plt.plot(s2.position, s2.saturation, 'r-o')

plt.xlabel("distance from injection face")
plt.ylabel("non-wetting phase saturation");
../../../_images/e5e29044a0911da345e72111884e035139e6fd93ab6cf2cf4e4b2e94af52c3a6.png

axis#

The direction along with the averaging window moves. This can be perpendicular to the axis where the injection occurred to give additional insights into the saturation distribution:

s1 = ps.metrics.satn_profile(satn=inv.im_satn, s=0.6, axis=0)
plt.plot(s1.position, s1.saturation, 'b-o')

s = 10
s2 = ps.metrics.satn_profile(satn=inv.im_satn, s=0.6, axis=1)
plt.plot(s2.position, s2.saturation, 'r-o')

plt.xlabel("distance from injection face")
plt.ylabel("non-wetting phase saturation");
../../../_images/f4f62e432f4324fe5a6d0af179c7194c801072b57c511d3f28a0f7d14d74cd43.png