def test_interpolate_wo():
"""Discrete test scenarios for wateroil interpolation"""
swl_l = random.uniform(0, 0.1)
swcr_l = swl_l + random.uniform(0, 0.1)
sorw_l = random.uniform(0, 0.2)
swl_h = random.uniform(0, 0.1)
swcr_h = swl_h + random.uniform(0, 0.1)
sorw_h = random.uniform(0, 0.2)
wo_low = WaterOil(swl=swl_l, swcr=swcr_l, sorw=sorw_l, h=0.001)
wo_high = WaterOil(swl=swl_h, swcr=swcr_h, sorw=sorw_h, h=0.001)
wo_low.add_corey_water(nw=random.uniform(1, 3),
krwend=random.uniform(0.5, 1))
wo_high.add_corey_water(nw=random.uniform(1, 3),
krwend=random.uniform(0.5, 1))
wo_low.add_corey_oil(now=random.uniform(1, 3),
kroend=random.uniform(0.5, 1))
wo_high.add_corey_oil(now=random.uniform(1, 3),
kroend=random.uniform(0.5, 1))
wo_low.add_simple_J(a=random.uniform(0.1, 2), b=random.uniform(-2, -1))
wo_high.add_simple_J(a=random.uniform(0.1, 2), b=random.uniform(-2, 1))
print(" ** Low curve (red):\n" + wo_low.swcomment + wo_low.krwcomment +
wo_low.krowcomment + wo_low.pccomment)
print(" ** High curve (blue):\n" + wo_high.swcomment + wo_high.krwcomment +
wo_high.krowcomment + wo_high.pccomment)
from matplotlib import pyplot as plt
_, mpl_ax = plt.subplots()
wo_low.plotkrwkrow(mpl_ax, color="red")
wo_high.plotkrwkrow(mpl_ax, color="blue")
for tparam in np.arange(0, 1, 0.1):
wo_ip = utils.interpolate_wo(wo_low, wo_high, tparam, h=0.001)
wo_ip.plotkrwkrow(mpl_ax, color="green")
mpl_ax.set_title("WaterOil, random Corey, linear y-scale")
plt.show()
# Plot again with log yscale:
_, mpl_ax = plt.subplots()
wo_low.plotkrwkrow(mpl_ax, color="red")
wo_high.plotkrwkrow(mpl_ax, color="blue")
for tparam in np.arange(0, 1, 0.1):
wo_ip = utils.interpolate_wo(wo_low, wo_high, tparam, h=0.001)
wo_ip.plotkrwkrow(mpl_ax, color="green", logyscale=True)
mpl_ax.set_title("WaterOil, random Corey, log y-scale")
plt.show()
# Capillary pressure
_, mpl_ax = plt.subplots()
wo_low.plotpc(mpl_ax, color="red", logyscale=True)
wo_high.plotpc(mpl_ax, color="blue", logyscale=True)
for tparam in np.arange(0, 1, 0.1):
wo_ip = utils.interpolate_wo(wo_low, wo_high, tparam, h=0.001)
wo_ip.plotpc(mpl_ax, color="green", logyscale=True)
mpl_ax.set_title("WaterOil, capillary pressure")
plt.show()
def test_interpolate_wo_pc(swl, dswcr, dswlhigh, sorw, a_l, a_h, b_l, b_h):
"""
Generate two random WaterOil curves, interpolate pc between them
and check that the difference between each interpolant is small,
this essentially checks that we can go continously between the
two functions.
"""
wo_low = WaterOil(swl=swl, swcr=swl + dswcr, sorw=sorw)
wo_high = WaterOil(swl=swl + dswlhigh,
swcr=swl + dswlhigh + dswcr,
sorw=max(sorw - 0.01, 0))
wo_low.add_corey_water()
wo_high.add_corey_water()
wo_low.add_corey_oil()
wo_high.add_corey_oil()
wo_low.add_simple_J(a=a_l, b=b_l)
wo_high.add_simple_J(a=a_h, b=b_h)
ips = []
ip_dist = 0.05
for t in np.arange(0, 1 + ip_dist, ip_dist):
wo_ip = utils.interpolate_wo(wo_low, wo_high, t)
check_table(wo_ip.table)
ips.append(wo_ip)
assert 0 < wo_ip.crosspoint() < 1
# Distances between low and interpolants:
dists = [(wo_low.table - interp.table)[["pc"]].sum().sum()
for interp in ips]
assert np.isclose(dists[0], 0)
# Distance between high and the last interpolant
assert (wo_high.table - ips[-1].table)[["pc"]].sum().sum() < 0.01
# Distances between low and interpolants:
dists = [(wo_low.table - interp.table)[["pc"]].sum().sum()
for interp in ips]
print("Interpolation, mean: {}, min: {}, max: {}, std: {} ip-par-dist: {}".
format(np.mean(dists), min(dists), max(dists),
np.std(np.diff(dists[1:])), ip_dist))
assert np.isclose(dists[0], 0) # Reproducing wo_low
# All curves that are close in parameter t, should be close in sum().sum().
# That means that diff of the distances should be similar,
# that is the std.dev of the distances is low:
ip_dist_std = np.std(np.diff(
dists[1:])) # This number depends on 'h' and 't' range
# (avoiding the first which reproduces go_low
if ip_dist_std > 1.0: # Found by trial and error
print("ip_dist_std: {}".format(ip_dist_std))
print(dists)
from matplotlib import pyplot as plt
_, mpl_ax = plt.subplots()
wo_low.plotpc(mpl_ax=mpl_ax, color="red", logyscale=True)
wo_high.plotpc(mpl_ax=mpl_ax, color="blue", logyscale=True)
for interp in ips:
interp.plotpc(mpl_ax=mpl_ax, color="green", logyscale=True)
plt.show()
assert False
def interpolation_art(repeats=50, interpolants=30, curvetype="corey"):
"""This code was used to create the Pyscal logo"""
from matplotlib import pyplot as plt
cmap = plt.get_cmap("viridis")
_, mpl_ax = plt.subplots()
for _ in range(repeats):
swl = random.uniform(0, 0.1)
swcr = swl + random.uniform(0, 0.1)
sorw = random.uniform(0, 0.2)
wo_low = WaterOil(swl=swl, swcr=swcr, sorw=sorw)
wo_high = WaterOil(swl=swl + 0.1, swcr=swcr + 0.1, sorw=sorw + 0.1)
if curvetype == "corey":
wo_low.add_corey_water(nw=random.uniform(1, 3),
krwend=random.uniform(0.5, 1))
wo_high.add_corey_water(nw=random.uniform(1, 3),
krwend=random.uniform(0.5, 1))
wo_low.add_corey_oil(now=random.uniform(1, 3),
kroend=random.uniform(0.5, 1))
wo_high.add_corey_oil(now=random.uniform(1, 3),
kroend=random.uniform(0.5, 1))
elif curvetype == "let":
wo_low.add_LET_water(
l=random.uniform(1, 3),
e=random.uniform(1, 3),
t=random.uniform(1, 3),
krwend=random.uniform(0.5, 1),
)
wo_high.add_LET_water(
l=random.uniform(1, 3),
e=random.uniform(1, 3),
t=random.uniform(1, 3),
krwend=random.uniform(0.5, 1),
)
wo_low.add_LET_oil(
l=random.uniform(1, 3),
e=random.uniform(1, 3),
t=random.uniform(1, 3),
kroend=random.uniform(0.5, 1),
)
wo_high.add_LET_oil(
l=random.uniform(1, 3),
e=random.uniform(1, 3),
t=random.uniform(1, 3),
kroend=random.uniform(0.5, 1),
)
else:
print("ERROR, wrong curvetype")
color = cmap(random.random())
for tparam in np.arange(0, 1, 1.0 / interpolants):
wo_ip = utils.interpolate_wo(wo_low, wo_high, tparam)
wo_ip.plotkrwkrow(mpl_ax, color=color, alpha=0.3)
plt.show()
def test_ip_kromax():
"""Test behaviour of kroend/kromax under interpolation"""
wo_low = WaterOil(swl=0, swcr=0.1, sorw=0.2)
wo_low.add_corey_water(nw=2, krwend=0.5, krwmax=0.7)
wo_low.add_corey_oil(now=2, kroend=0.6, kromax=0.9)
wo_high = WaterOil(swl=0.02, swcr=0.05, sorw=0.1)
wo_high.add_corey_water(nw=2, krwend=0.5, krwmax=0.72)
wo_high.add_corey_oil(now=2, kroend=0.6, kromax=0.8)
# Interpolate to midpoint between the curves above
wo_ip = utils.interpolate_wo(wo_low, wo_high, 0.5)
# kromax at mean swl:
assert float_df_checker(wo_ip.table, "sw", 0.01, "krow", 0.85)
# kroend is 0.6 in low and high, but swcr has changed:
assert float_df_checker(wo_ip.table, "sw", 0.075, "krow", 0.6)
assert float_df_checker(wo_ip.table, "sw", 1, "krw", 0.71)
assert float_df_checker(wo_ip.table, "sw", 1 - 0.15, "krw", 0.5)
def test_tag_preservation():
"""Test that we can preserve tags/comments through interpolation"""
wo_low = WaterOil(swl=0.1)
wo_high = WaterOil(swl=0.2)
wo_low.add_corey_water(nw=2)
wo_high.add_corey_water(nw=3)
wo_low.add_corey_oil(now=2)
wo_high.add_corey_oil(now=3)
interpolant1 = utils.interpolate_wo(wo_low, wo_high, parameter=0.1, h=0.2)
assert "Interpolated to 0.1" in interpolant1.tag
sat_table_str_ok(interpolant1.SWOF())
wo_high.tag = "FOOBAR"
interpolant2 = utils.interpolate_wo(wo_low, wo_high, parameter=0.1, h=0.2)
assert "Interpolated to 0.1" in interpolant2.tag
assert "between" in interpolant2.tag
assert wo_high.tag in interpolant2.tag
sat_table_str_ok(interpolant2.SWOF())
# wo_low.tag was empty deliberately here.
# When wo_log and wo_high has the same tag:
wo_low.tag = "FOOBAR"
interpolant3 = utils.interpolate_wo(wo_low, wo_high, parameter=0.1, h=0.2)
assert "Interpolated to 0.1" in interpolant3.tag
assert "between" not in interpolant3.tag
assert wo_high.tag in interpolant3.tag
sat_table_str_ok(interpolant3.SWOF())
# Explicit tag:
interpolant4 = utils.interpolate_wo(wo_low,
wo_high,
parameter=0.1,
h=0.2,
tag="Explicit tag")
assert interpolant4.tag == "Explicit tag"
# Tag with newline
interpolant6 = utils.interpolate_wo(wo_low,
wo_high,
parameter=0.1,
h=0.2,
tag="Explicit tag\non two lines")
assert "Explicit tag" in interpolant6.tag
print(interpolant6.SWOF())
sat_table_str_ok(interpolant6.SWOF())
# Empty tag:
interpolant5 = utils.interpolate_wo(wo_low,
wo_high,
parameter=0.1,
h=0.2,
tag="")
assert interpolant5.tag == ""
# Also sample check for GasOil (calls the same code)
go_low = GasOil()
go_high = GasOil()
go_low.add_corey_gas(ng=2)
go_high.add_corey_gas(ng=3)
go_low.add_corey_oil(nog=2)
go_high.add_corey_oil(nog=3)
interpolant1 = utils.interpolate_go(go_low, go_high, parameter=0.1, h=0.2)
assert "Interpolated to 0.1" in interpolant1.tag
sat_table_str_ok(interpolant1.SGOF())
def interpolate(self, parameter, parameter2=None, h=0.02):
"""Interpolate between low, base and high
parameter = -1 reproduces low curve
parameter = 0 reproduces base curve
parameter = 1 reproduces high curve
Endpoints are located for input curves, and interpolated
individually. Interpolation for the nonlinear part
is done on a normalized interval between the endpoints
Interpolation is linear in relperm-direction, and will thus not be
linear in log-relperm-direction
This method returns an WaterOilGas object which can be
realized into printed tables. No attempt is made to
parametrize the interpolant in L,E,T parameter space, or Corey-space.
If a second parameter is supplied ("parameter2") this is used
for the gas-oil interpolation. This enables the gas-oil
interpolant to be fully uncorrelated to the water-oil
interpolant. CHECK how this affects endpoints and Eclipse
consistency!!
"""
if parameter2 is not None:
gasparameter = parameter2
else:
gasparameter = parameter
# Either wateroil or gasoil can be None in the low, base, high
# If they are None, it is a two-phase problem and we
# should support this.
do_wateroil = (self.base.wateroil is not None
and self.low.wateroil is not None
and self.high.wateroil is not None)
do_gasoil = (self.base.gasoil is not None
and self.low.gasoil is not None
and self.high.gasoil is not None)
if not do_wateroil and not do_gasoil:
raise ValueError(
"Neither wateroil or gasoil is complete in SCAL recommendation"
)
if parameter2 is not None and not do_gasoil:
logger.warning("parameter2 is meaningless for water-oil only")
# Initialize wateroil and gasoil curves to be filled with
# interpolated curves:
tags = set()
if do_wateroil:
tags = tags.union(
set([
self.base.wateroil.tag,
self.low.wateroil.tag,
self.high.wateroil.tag,
]))
if do_gasoil:
tags = tags.union(
set([
self.base.gasoil.tag, self.low.gasoil.tag,
self.high.gasoil.tag
]))
tagstring = "\n".join(tags)
interpolant = WaterOilGas(h=h, tag=tagstring)
if do_wateroil:
tag = (
"SCAL recommendation interpolation to {}\n".format(parameter) +
tagstring)
if abs(parameter) > 1.0:
logger.error("Interpolation parameter must be in [-1,1]")
assert abs(parameter) <= 1.0
elif np.isclose(parameter, 0.0):
interpolant.wateroil = self.base.wateroil
interpolant.wateroil.tag = tag
elif np.isclose(parameter, -1.0):
interpolant.wateroil = self.low.wateroil
interpolant.wateroil.tag = tag
elif np.isclose(parameter, 1.0):
interpolant.wateroil = self.high.wateroil
interpolant.wateroil.tag = tag
elif parameter < 0.0:
interpolant.wateroil = utils.interpolate_wo(self.base.wateroil,
self.low.wateroil,
-parameter,
h=h,
tag=tag)
elif parameter > 0.0:
interpolant.wateroil = utils.interpolate_wo(self.base.wateroil,
self.high.wateroil,
parameter,
h=h,
tag=tag)
else:
interpolant.wateroil = None
if do_gasoil:
tag = ("SCAL recommendation interpolation to {}\n".format(
gasparameter) + tagstring)
if abs(gasparameter) > 1.0:
logger.error("Interpolation parameter must be in [-1,1]")
assert abs(gasparameter) <= 1.0
elif np.isclose(gasparameter, 0.0):
interpolant.gasoil = self.base.gasoil
interpolant.gasoil.tag = tag
elif np.isclose(gasparameter, -1.0):
interpolant.gasoil = self.low.gasoil
interpolant.gasoil.tag = tag
elif np.isclose(gasparameter, 1.0):
interpolant.gasoil = self.high.gasoil
interpolant.gasoil.tag = tag
elif gasparameter < 0.0:
interpolant.gasoil = utils.interpolate_go(self.base.gasoil,
self.low.gasoil,
-1 * gasparameter,
h=h,
tag=tag)
elif gasparameter > 0.0:
interpolant.gasoil = utils.interpolate_go(self.base.gasoil,
self.high.gasoil,
gasparameter,
h=h,
tag=tag)
else:
interpolant.gasoil = None
return interpolant
