Page 270 - Geology of Carbonate Reservoirs

CONCLUSIONS 251

In a hypothetical example, let the most representative range of average porosity
in the entire reservoir sample be 5 – 35% and the average permeability range be
15 – 105 md. The average porosity values for the reservoir can be divided arbitrarily

into three categories of 10% each, or 5 – 15%, 16 – 25%, and 26 – 35%. Permeability

values can be divided into three categories of 15 – 45, 46 – 75, and 76 – 105 md. Then,

constructing a symmetrical matrix of 3 × 3 (or 9) possible values for porosity and

permeability pairs simplifies the selection process for best, intermediate, and poor

ranking.
After the ranking matrix is constructed from poroperm data, the portions of
each 10 - foot slice that are best, intermediate, and poor in rank can be identifi ed
within the field. One method is to overlay contour maps of average porosity and

average permeability for each 10 - foot interval of vertical thickness and locate the
areas where contours of high average porosity overlie areas of high average per-
meability, and so on (Ahr and Hammel, 1999 ). Around boreholes where cores
were taken and poroperm values were measured directly, accuracy is not a
problem. Where no cores were taken, porosity must be estimated from log cal-
culations. If no pressure tests are available to determine permeability in those
wells lacking cores, then permeability must be estimated by comparing poroperm
data from core analyses with noncored well wireline log data. Linear regressions
comparing porosity and permeability from core analyses, and then from wireline
log data, can provide estimates of permeability for noncored wells. Map overlays
of porosity, permeability, and facies character enable one to identify best, inter-
mediate, and poor flow units, baffles, and barriers — the latter two being rocks that

have porosity and permeability values below the limits used in the quality ranking
matrix.

If reservoir flow units do not correspond to depositional facies characteristics
then rock samples must be examined to determine the degree and kind of diagenesis
that has altered them. Has diagenesis increased or decreased original depositional
porosity? By what processes? Finally, after determining how much and what kind
of diagenetic change has taken place, it only remains to determine the chronological
history of diagenetic events that led to present - day reservoir quality. Did replace-
ment or dissolution infl uence flow unit quality, for example? Was dissolution early

or late in burial history? Is the imprint of dissolution cross - cut by a later form of
diagenesis? What is the impact of each of the diagenetic events on the fi nal

outcome — today ’ s reservoir flow unit quality?
Fractured reservoirs are special cases that require special methods of analysis.
Fractures may contribute essential porosity and permeability or not. They may infl u-
ence reservoir performance in a positive or negative manner. They may be caused
by a variety of mechanical processes, but the fact that they are formed mechanically
as deformation due to stress is incontrovertible. They are not depositional or dia-
genetic. Determining reservoir quality in fractured systems is at least partly a process
of determining whether the reservoir is Type I , Type II, Type III, or Type IV fractured
system according to Nelson ( 2001 ). The size and shape of fractured rock bodies in
the subsurface can probably be estimated by evaluating pressure transient data with
methods such as Horner plots. There is no reason to expect fractured zones to cor-
respond with facies maps or with types of diagenesis, although it is possible to some
extent.