The usual approach to calibrating
permeability is to crossplot core porosity versus
permeability and obtain a regression line. Unfortunately
this presumes that the rock type does not change over the
interval. But the technique is widely used anyway. The
porosity - irreducible water saturation (Wyllie-Rose)
equation offers some improvement when pore geometry varies,
but regression is more difficult due to the multiple
A poor quality regression because
data is clustered,
Good quality regression with a wide spread in porosity. because data has a good distribution of porosity.
there is more than one rock type in the interval, several trend
lines may be evident. If data is a large splash on the plot, try
to reduce scatter by zoning rock types. In fractured reservoirs,
some data points with high perm and low porosity should be excluded
from the curve fitting so that a matrix permeability is obtained
separately from fracture permeability.
good feel for the quality or usefulness of log analysis permeability
can be obtained by crossplotting predicted productivity with actual
initial productivity. Average the third to ninth month production
to get a realistic initial production value. Calibrating to this
data will compensate for completion hardware, stimulation, fluid
type, and reservoir conditions that could not be handled with
our simplified math. This is not a very reliable approach, especially
in fractured reservoirs, but it is better than not checking.
stem test flow rates, AOF, and IPR data can also be used. Be careful
to compare log analysis results from only the tested interval.
best calibration tool is feedback from a reservoir simulation.
If a history match can be obtained based on the reservoir description,
all is well. If reservoir volume has to be augmented or permeability
doubled to get a match, then the log analysis or the reservoir
maps need help. Frank and intelligent discussion between all disciplines
in the analysis team will usually find where calibration is still
A depth plot comparison of
log analysis versus core analysis permeability provides a good
comparison method, often more useful than regression. The
calibration can be done by trial and error, varying the parameters
as needed to get a better match.
Good match between log and core
porosity (Track 5). Usually adjusting the constant term in the
permeability equation will move the log analysis result enough to
match the core data. The other exponents seldom need to be changed.
Somewhat poorer match between log and core permeability due to very
thinly laminated porosity - permeability environment. Since the logs
average a 3 foot interval, and the core data can be measured at a
much finer increment, a perfect match is not possible.
However, a slightly higher constant term would
improve the calibration.
ines are log analysis.
Bakken “Tight Oil” example showing core porosity (black dots), core oil saturation (red dots).
core water saturation (blue dots), and permeability (red dots). Note
excellent agreement between log analysis and core data. Separation
between red dots and blue water saturation curve indicates
significant moveable oil, even though water saturation is relatively
high. Log analysis porosity is from the complex lithology model and
lithology is from a 3-mineral PE-D-N model using quartz, dolomite