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CAPILLARY PRESSURE  Pc
Capillary pressuRE Accumulation of hydrocarbon in a reservoir is a drainage process and production by aquifer drive or waterflood is an imbibition process. The capillary pressure curve is different for these two processes. phase increases. Mobility of nonwetting fluid phase increases as nonwetting phase saturation increases. IMBIBITION phase increases. Mobility of wetting phase increases as wetting phase saturation increases There are four key parameters that are related to a capillary curve :
Si =
irreducible wetting phase saturation
Sm
= 1  residual nonwetting phase
saturation
Pd
= displacement pressure, the pressure
required to force nonwetting fluid into largest pores
LAMDA = pore size
distribution index; determines shape of capillary pressure
curve
Si is the initial water saturation in a reservoir. It is termed SWir, the irreducible water saturation, elsewhere in this Handbook. (1  Sm) is the residual oil saturation in a water wet reservoir, called Soil or Sor elsewhere in this Handbook. Petrophysicists use cap pressure minimum saturation (SWir) and residual oil saturation (Sor) to help calibrate log derived water saturation in oil and gas reservoirs above the transition zone, and to help detect depleted reservoirs. It will not help calibrate SW in partially depleted zones.
LAMDA increases with decreasing permeability, poor grain sorting, smaller grain size, and usually with lower porosity. These effects shift the cap pressure curve upward and to the right, resulting in higher SWir values.
A
capillary pressure curve on Cartesian coordinates is
difficult to fit with simple equations. By transforming the
SW axis to Sw* and plotting Pc vs Sw* on log  log graph
paper, the curves become straight lines.
1: Sw* = (Sw  SWir) / (1  SWir  Sor) 2: Pc = Pd * (Sw*) ^ (1 / LAMDA) The slope of the line is (1 / LAMBDA) and the intercept at 100% Sw* is Pd. SWir is obtained from the Cartesian plot. Steeper slope equals higher LAMDA equals poorer quality rock. The typical range of LAMDA is 0.5 for good quality sands to 4 or 8 for poor quality sands and carbonates. Type curve matching of Sw* can be used to assess cap pressure curves and reservoir quality.
If
a glass capillary tube is placed in a large open vessel containing
water, the combination of surface tension and wettability of tube to
water will cause water to rise in the tube above the water level in
the container outside the tube. The water will rise in the tube
until the total force acting to pull the liquid upward is balanced
by the weight of the column of liquid being supported in the tube.
Assuming the radius of the capillary tube is R, the total upward
force Fup, which holds the liquid up, is equal to the force per unit
length of surface times the total length of surface:
Assume density of air is negligible and set Fup = Fdown, solve
for surface tension:
Where:
Rearranging equation 5:
Where:
To convert Pc in dynes/cm2 to psi, multiply by 1.45 * 10^5.
Typical
values for airbrine conversion to oilwater are:
Solving equation 13 for H, and using reservoir (oilwater) Pc
values:
Where:
<== Example of conversion of lab airbrine capillary
pressure data to reservoir conditions, then into saturationheight
H; results plotted in graph above..
All of the above assumes the lab data is an airbrine
measurement. For mercury injection capillary, pressure (MICP) measurements,
the density of the nonwetting phase (mercury) is 13.5 g/cc, so ΔDENS
is much larger than the airwater case. As a result, Pc values from
an MICP measurement are about 12.5 times higher than an air brine
measurement (for the same SW value in the same core plug). To
compare an airbrine cap pressure curve to an MICP curve, it is
merely necessary to change the Pc scale on one of the graphs by the
appropriate factor, or to convert both Pc scales to a
saturationheight scale.
MEASURING
Capillary pressure
Porous diaphragm
method
• Mercury injection method
•
Centrifuge method
Dynamic
method
Detailed operation of the laboratory equipment is beyond the scope of this Handbook. The illustrations are not fully selfexplanatory, but the general principles are relatively visible.
POROUS DIAPHRAGM METHOD
MERCURY INJECTION METHOD
CENTRIFUGAL METHOD
AVERAGING
Capillary pressure
The Leverett Jfunction was originally an attempt to convert all capillary pressure data to a universal curve. •A universal capillary pressure curve does not exist because the rock properties affecting capillary pressures in reservoir have extreme variation with lithology (rock type).• But, Leverett’s Jfunction has proven valuable for correlating capillary pressure data within a lithologic rock type. The
Leverett JFunction is described by By substitution and
rearrangement:
•Jfunction is
useful for averaging capillary pressure data from a
given rock type from a given reservoir and
•can
sometimes
be extended to different reservoirs having the same
lithology.
Use extreme
caution in assuming this can be done.
Jfunction is usually not an accurate
correlation for different lithologies.
If Jfunctions are not successful
in reducing the scatter in a given set of data, then
this suggests that we are dealing with variation in
rock type.
In higher permeability rock, the cap pressure curve quickly reaches an asymptote and the minimum saturation usually represents the actual water saturation in an undepleted hydrocarbon reservoir above the transition zone. In tight rock, the asymptote is seldom reached, so we pick saturation values from the cap pressure curves at two heights (or equivalent) Pc values) to represent two extremes of reservoir condition. Only sample 1 in the above table behaves close to asymptotically, as in curve A in the schematic illustration at the right. All other samples behave like curves B and C (or worse). The real cap pressure curves for samples 1 and 2 are shown below.
The summary table shows wetting phase saturation selected by observation of the cap pressure graphs at two different heights above free water, namely 100 meters and 425 meters in this example. In this case, the 100 meter data gives water saturations that we commonly see in petrophysical analysis of well logs in hydrocarbon bearing Bakken reservoirs in Saskatchewan. This is a pragmatic way to indicate the water saturation to be expected when a Bakken reservoir is at or near irreducible water saturation. The data for the 450 meter case is considerably lower and probably does not represent reservoir conditions in this region of the Williston Basin. Two other columns in the table are calculated from the primary measurements. The first is the product of porosity times saturation, PHI*SW, often called Buckle’s Number. It is considered to be a measure of pore geometry or grain size. Higher values are finer grained rocks. These values vary considerably in the Bakken, between low and medium values, indicating the laminated nature of the silt / sand reservoir. The values in the Torquay are uniformly high, indicating that the reservoir is poor quality in all samples. The second is the square root of permeability divided by porosity, sqrt(Kmax/PHIe), which is another measure of reservoir quality, directly proportional to pore throat radius and Pc. High numbers represent good connectivity and low values show poor connectivity. Again, the Bakken shows the variations due to laminations, and the Torquay shows low values and unattractive reservoir quality. Average pore throat radius and detailed pore throat distribution data are now routinely available in the capillary pressure spreadsheet provided by the core analysis laboratory. Examples are shown below.
By comparing cap pressure and pore throat distribution graphs from each sample with the quality indicator values in the summary table, it becomes more evident as to which parameters in a petrophysical analysis might be the best indicator of reservoir quality. Since both Buckle’s Number and the Kmax/PHIe parameter can be determined from logs, it has been relatively common to assess reservoir quality from these parameters as a proxy for capillary pressure and pore throat measurements. However, in thinly laminated reservoirs like the Bakken, this is not always possible since the logging tools average 1 meter of rock. This means we cannot see the internal variations of rock quality evident in the core data.


Copyright ©
E. R. (Ross) Crain, P.Eng.
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