def crosspoint(self):
"""Calculate the sw value where krg == krw.
Accuracy of this crosspoint depends on the resolution chosen
when initializing the saturation range (it uses linear
interpolation to solve for the zero)
Returns:
float: the gas saturation where krw == krg, for relperm
linearly interpolated in water saturation.
"""
if not {"sw", "krw"}.issubset(self.wateroil.table.columns):
logger.warning("Can't compute crosspoint when krw is not present")
return None
if not {"sl", "krg"}.issubset(self.gasoil.table.columns):
logger.warning("Can't compute crosspoint when krg is not present")
return None
dframe = pd.concat(
[
self.wateroil.table[["sw", "krw"]],
self.gasoil.table[["sl", "krg"]]
],
sort=False,
)
# The "sl" column in the GasOil object corresponds exactly to "sw" in WaterOil
# but since they are floating point, we do not want to "merge" dataframes on it,
# rather concatenate and let linear interpolation fill in values.
dframe["sw"].fillna(value=0, inplace=True)
dframe["sl"].fillna(value=0, inplace=True)
dframe["sat"] = dframe["sl"] + dframe["sw"]
dframe = (dframe.set_index("sat").sort_index().interpolate(
method="slinear").dropna().reset_index())
return crosspoint(dframe, "sat", "krw", "krg")
def crosspoint(self):
"""Locate and return the saturation point where krw = krow
Accuracy of this crosspoint depends on the resolution chosen
when initializing the saturation range
Returns:
float: the water saturation where krw == krow, for relperm
linearly interpolated in water saturation.
"""
return crosspoint(self.table, "sw", "krw", "krow")
def crosspoint(self) -> float:
"""Locate and return the saturation point where krg = krog
Accuracy of this crosspoint depends on the resolution chosen
when initializing the saturation range (it uses linear
interpolation to solve for the zero)
Returns:
The gas saturation where krg == krog, for relperm
linearly interpolated in gas saturation.
"""
return crosspoint(self.table, "SG", "KRG", "KROG")
