
Publication History:
This article was written
especially for "Crain's Petrophysical Handbook"
by E. R. Crain,
P.Eng in 2007. This
webpage version is the copyrighted intellectual
property of the author.
Do not copy or distribute in any form without explicit
permission. 
PYRITE CORRECTIONS
Pyrite is a
conductive metallic mineral that may occur in many different
sedimentary rocks. It can reduce measured resistivity, thus
increasing apparent water saturation. The conductive metallic
current path is in parallel with the ionic water conductive
path. As a result, a correction to the measured resistivity can
be made by solving the parallel resistivity circuit.
Although the math is simple, the parameters needed are not well
known. The two critical elements are the volume of pyrite and the
effective resistivity of pyrite. Pyrite volume can be found from a
two or three mineral model,
calibrated by thin section point counts or Xray diffraction data.
The
resistivity of pyrite varies with the frequency of the logging tool
measurement system. Laterologs measure resistivity at less than 100
Hz, induction logs at 20 KHz, and LWD tools at 2 MHz. Higher
frequency tools record lower resistivity than low frequency tools
for the same concentration of pyrite. The variation in resistivity
is caused by the fact that pyrite is a semiconductor, not a metallic
conductor. It is nature's original transistor, and formed the main
sensing component in early radios.
Typical resistivity of pyrite
is in the range of 0.1 to 1.0 ohmm; 0.5 ohmm seems to work
reasonably well. The effect of pyrite is most noticeable when RW is
moderately high and less noticeable when RW is very low.
The
math is easiest when conductivity is used instead of resistivity:
1: CONDpyr = 1000 / RESpyr
2: CONDcorr = 1000 / RESD  CONDpyr * Vpyr
3: RESDcorr = 1000 / CONDcorr
The corrected resistivity can be plotted versus depth, along
with the original log.
Corrected water saturation will always be lower or equal to the
original Sw.
If CONDcorr goes negative, lower Vpyr or raise RESpyr
Modeling the Effect of Pyrite
It
is often instructive to model the effects of pyrite to see what
happens to the resistivity and water saturation. The method
below is aided by a spreadsheet, shown later on this ppage.
Step 1: Model resistivity of water and
hydrocarbon zones with no pyrite, using PHIe from log analysis
of the recorded log data, with Sw from capillary pressure or common
sense:
1: Ro = A * RW / (PHIe ^ M)
2: Rt = (A * RW / (PHIe ^ M)) / (Sw ^ N)
Step 2:
Convert these to conductivity
3: Co = 1000 / Ro
4: Ct = 1000 / Rt
Step 3:
Model conductivity of pyrite and solve for resistivity of
water+pyrite and hydrocarbon+pyrite, using parallel circuit theory:
5: Cpyr = 1000 / Rpyr * Vpyr
6: Cwet = Co + Cpyr
7: Chyd = Ct + Cpyr
Step 4:
Convert conductivity to resistivity:
8: Rwet = 1000/ Cwet
9: Rhyd = 1000 / Chyd
Compare these resistivities to the original Ro and Rt to see the
effect of pyrite.
Step 5: To correct an existing log
analysis, calculate uncorrected and corrected water saturation:
10: SwUnCorr = ((A * RW / ((PHIe ^ M) * RESD)) ^ (1/N))
11: Cpyr = 1000 / Rpyr * Vpyr
12: CONDcorr = 1000 / RESD  Cpyr
13: SwCorr = ((A * RW / ((PHIe ^ M) * 1000 / CONDcorr))
^ (1/N))
Compare SwUnCorr and SwCorr to see the effect of pyrite.
COMMENTS:
The corrected resistivity can be plotted versus depth, along
with the original log.
Corrected water saturation will always be lower or equal to the
original Sw.
If CONDcorr goes negative, lower Vpyr or raise Rpyr
RECOMMENDED PARAMETERS:
Rpyr is in the range 0.1 to 1.0 ohmm, default 0.5.
Vpyr is usually in the range 0.00 to 0.08, default 0.03
"META/PYR"
SPREADSHEET  Modeling Pyrite Effect on Resistivity Logs
This spreadsheet models the effects of pyrite on
resistivity and saturation values. The model can be used to assess
the effect of varying pyrite quantity, pyrite resistivity, water
resistivity, and porosity.
Model
Effects of Pyrite on Resistivity and Saturation.
Sample of "META/PYR" Spreadsheet used for modeling the effect of
pyrite on resistivity.
Resistivity 
Saturation in Laminated Reservoirs
The analysis models for laminated shaly sands are quite varied
and none are perfect solutions. This topic is covered more fully
elsewhere in this Handbook The
problem lies in how logs average laminations that are thinner
than the tool resolution. Most logs average the data in a linear
fashion, but resistivity must be averaged as conductivity and
then converted back to resistivity. This is the situation with
most socalled “lowresistivity” pay zones around
the world.
The
problem exists in laminated shaly sands and in clean sands or
carbonates where porosity is laminated. The shale laminations or the
low porosity laminations (with high water saturation) have
considerably higher conductivity than the cleaner, higher porosity
laminations. The net result is a low resistivity reservoir. The
chance of bypassing such zones is quite high.
To illustrate, assume a laminated sequence with shale laminations
equal in thickness to the sand laminations. This gives a shale
volume (Vsh) averaged over the interval of 50%. Assume the porosity
and resistivity values are as shown below:
GAS
SAND 
GR 
PHIN 
PHID 
RESD 
COND 
RESD
from COND 
Shale 
90 
0.45 
0.15 
4.0 
250 

Gas
Sand 
40 
0.25 
0.35 
200 
5.0 

Average 
65 
0.30 
0.25 
102 
127 
7.9 








WTR
SAND 
GR 
PHIN 
PHID 
RESD 
COND 
RESD
from COND 
Shale 
90 
0.45 
0.15 
4.0 
250 

Water
Sand 
40 
0.30 
0.30 
5.0 
200 

Average 
65 
0.37 
0.22 
4.5 
222 
4.2 

The
resistivity in this gas zone is only 8 ohmm. It surprises many
people that the average of 4 and 200 is only 8! The contrast with
the water zone is low, so many laminated zones are bypassed as
not being worthy of completion.
In
the early days of log analysis, this phenomenon was attributed
to many different, almost mystical, reasons because the parallel
nature of the conductive paths was not understood by many analysts.
Low resistivity gas pay
In
the example above, density neutron crossover shows gas pay
in zones with horizontal resistivity of less than 3.0 ohmm. This crossover
is not caused by bad hole conditions or inappropriate density
neutron porosity scales. The vertical resistivity shows the
effect of the gas, while the horizontal resistivity is dominated
by the conductive shale laminations.
While
gas zones stand out because of gas effect on the density neutron,
oil zones will not be so obvious. Pay attention to sample descriptions,
oil shows in core or on the mud pit, and the mud log, especially
the higher C4+ curves.
Water
saturation cannot be calculated in the usual way in laminated
shaly sands. Sometimes porosity cannot be determined explicitly
either.
Formation
microscanner and acoustic televiewer logs help to obtain a good
sand count. Sand porosity is often determined from core analysis
and water saturation calculated from the Buckle's method.
Water Saturation in Fractured Rocks
Water saturation in fractured reservoirs is a complex issue and
has a huge literature. Fractured reservoirs in general are
covered elsewhere in this Handbook, where the dual porosity
model for fractured reservoirs is covered. This includes a water
saturation method that is widely used.
The
major complication is that a large fracture will be deeply invaded
by mud filtrate and will be full of water at logging time, yet
be full of oil or gas at static or producing conditions. The host
rock will not invade as deeply so the saturation distribution
while logging is not as neat or as predictable as in unfractured
rocks.
In
many situations, a simple approach using the Archie water
saturation equation with a low value for M is satisfactory. Use
the Pickett plot to determine M from a
shallow resistivity log. Careful zonation may be required to isolate
heavily fractured from less fractured intervals. If M seems to
be too high, then the shallow resistivity is seeing residual hydrocarbons.
The
variable M approach based on any one of the methods given in
previous Sections can also be used.
Pickett plot in a fractured reservoir
In
the Pickett plot shown above, lines for M = 2.0 and M = 1.4 are
drawn. It is clear that M varies between about 1.0 and 2.4. M
can be calculated for each data point and used in the saturation
equation.
