Lab 4
Net pay determination using core data
Objectives
To demonstrate methods of selecting net pay using core data
Background
Previously, we have shown that net pay and the average porosity and saturation of this
net pay, are critical components to the estimation of hydrocarbons in place. In this
chapter we will define and apply cutoff methods to estimate net pay using core data. The
results are essential to proper analysis of hydrocarbons in place by well and also by
reservoir.
Figure 1 illustrates the definition of net pay. The gross thickness is defined as the
interval from the top to the bottom of the reservoir including all non-reservoir rock such
as shales, anhydrites, salts etc. The gross sand refers to the portion of the reservoir which
excludes the non-reservoir rock, thus is subject to some defined or arbitrary shale cutoff.
Apply
Sw cutoff
Apply
cutoff
Figure 1. Definition of reservoir intervals
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Apply
Vsh cutoff
The net sand is that fraction of the gross sand that is porous and permeable and contains
hydrocarbons and water, and is subject to a defined or arbitrary porosity cutoff. The net
pay is that component of the net sand which contains only hydrocarbons and is subject to
a water saturation cutoff.
Besides volumetric analysis, estimating net pay is useful in determining total reservoir
energy; which must included both movable and non-movable hydrocarbons. Nonmovable hydrocarbons exist in transition zones and below producing oil-water contacts,
and are traditionally not included in hydrocarbons in place calculations. Another use for
determining net pay is to evaluate the potential hydrocarbons available for secondary
recovery; e.g., waterflooding. In this case, the selection of net pay is biased towards
those intervals with favorable relative permeability to the injection fluids.
Estimation of Cutoff values
As discussed in the previous section, determination of net pay relies on the application of
reasonable cutoff values for shale content, porosity, and water saturation. Ideally,
production tests coupled with petrophysical data would provide the information to make
decisions about the appropriate cutoffs to apply. Unfortunately, this rarely occurs;
therefore in many cases the selection of a cutoff value is based on intuitive judgement.
An alternative is to apply cumulative distribution functions (cdf) to discriminate between
pay and non-pay. Examples will be shown later how this method works.
Porosity
The porosity cutoff is selected to provide sufficient absolute permeability to ensure
economic production. Determination of this value relies on generating a porosity –
permeability relationship from the core data. Figure 2 illustrates the relationship for
clastic rocks. In this case the influence of grain size dominates the results.
Figure 2. Influence of grain size on the porosity-permeability relationship for clastics
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Pore types (also referred to as rock type) also play an important role in influencing the k relationship. This is especially pronounced in carbonates where pore types are
extremely variable. Figure 3 illustrates the effect.
Figure 3. Influence of pore type on porosity-permeability relationship
In many cases, it is not possible to identify a single true correlation. Instead a “shotgun”
type pattern is visible. Figure 4 is such an example for the San Andres Formation in
West Texas.
All rock types
Core Permeability, md
100.0
10.0
1.0
0.1
5
10
15
20
25
30
Core Porosity
Figure 4. Core permeability – porosity for a San Andres example from West Texas
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The scatter is a response to the variety of rock types (and hence pore geometry) that is
lumped together. Figure 5 shows the same data but divided into four rock types. Notice
the improved correlation between permeability and porosity in this case. For a minimum
permeability of one md, the porosity cutoff varies from 7 to ~30% depending on the rock
type.
Rock type 1
Rock type 2
100
Core Permeability, md
Core Permeability, md
100
10
y = 0.0223e0.5231x
R2 = 0.9889
1
0.1
5
c=7%
10
y = 7E-05e0.7329x
2
R = 0.8607
1
0.1
10
15
20
25
30
5
10
Core Porosity
15
20
25
30
Core Porosity
Rock type 3
Rock type 4
100
Core Permeability, md
100
Core Permeability, md
c=13%
10
1
0.3519x
y = 0.0038e
R2 = 0.7655
0.1
5
10
c=16%
15
y = 0.0639e0.0827x
2
R = 0.3669
10
1
0.1
20
25
30
5
Core Porosity
10
15
20
25
30
Core Porosity
Figure 5. Data from Figure 4 divided into four rock types
After selecting a cutoff value, the next step is to apply this porosity cutoff to determine
flow capacity, storage capacity and net pay for the zone of interest. An approach is to
sort the variable of interest and then generate a cumulative function of net pay, storage or
flow capacity, or fraction of capacity. For example, core data was acquired in the
aeolian-deposited, Entrada Sandstone of Northwest New Mexico. Figure 6 displays the
excellent -k correlation for this formation. This is due to the well-sorted and uniform
behavior of Aeolian deposits.
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1000
Permeability, md
ka
100
kg
10
kh
1
0.1
0.01
0
5
10
15
20
25
30
35
Porosity, %
Figure 6. Porosity – permeability relationship for Entrada Sandstone
h
1
2
3
4
29.2
28.5
27.4
27.2
….
h
1
53
7.8
1208.6
29.2
57.7
85.1
112.3
h/hT
0.024
0.048
0.070
0.093
….
h
….
7.8
h, ft
1
1
1
1
….
….
Sorted

29.2
28.5
27.4
27.2
….
The next step is to sort the porosity in descending order as shown in Column 1 of figure
7. Each porosity value has an associated core thickness, h. These values are shown in
column 2. In this case, every foot of core is represented by a porosity value, thus the core
interval values are all one foot, and the total value is 53 feet. Storage capacity (h) for
each interval is calculated (Col. 4) and summed (Col. 5) to obtain the total storage
capacity. And last, the fraction of the total storage capacity (Col. 6) can be obtained by
dividing by the total (1208.6) storage capacity and thus normalizing to range from 0 to 1
1.000
Figure 7. Sorted porosity values for the core data from the Entrada example
It is possible to graph cumulative thickness, cumulative storage capacity (h) and/or
fraction of the storage capacity with respect to porosity. The latter is shown in Figure 8.
If the minimum permeability for flow is assumed to be 20 md, then from Figure 6, the
porosity cutoff is 15%. Applying this cutoff to Figure 8 results in 96% of the storage
capacity sufficient for flow. If the other graphs are generated than 49 ft of clean porous
sand or 12 ft of storage capacity will exceed the cutoff criteria.
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fraction of total storage
1.0
kh
0.9
0.8
0.7
0.6
kg
0.5
0.4
ka
0.3
0.2
0.1
0.0
0
5
10
15
20
Porosity, %
25
30
35
Figure 8. Fraction of total storage capacity as a function of sorted porosity for Entrada
Sandstone core
Similar calculations and graphs can be generated for flow capacity and fraction of flow
capacity. The latter is shown in Figure 9. For a minimum permeability of 20 md, almost
100% of the flow capacity is available.
1.0
kh
Fraction of flow capacity
0.9
0.8
0.7
0.6
kg
ka
0.5
0.4
0.3
0.2
0.1
0.0
0.1
1.0
10.0
100.0
1000.0
Permeability, md
Figure 9. Fraction of total flow capacity as a function of sorted permeability for Entrada
Sandstone core
Without the above method, typical assumed values for porosity cutoffs are 8% for
sandstones and 4% for carbonates; however these values are frequently revised. For Gulf
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Coast sands, 15% is more likely, while for Rocky Mountains a 5% porosity cutoff is not
unrealistic. Carbonates are even more difficult due to their variable pore types. Cutoff
values as low as 2% are not uncommon.
Water saturation
The water saturation cutoff ensures sufficient effective permeability to be productive. If
special core analysis is available then the ratio of relative permeabilities can identify the
saturation cutoff (Figure 10). However, typically this data is not available, therefore a
50% water saturation cutoff is assumed to be correct. This value could be greater for
water-wet rocks, water drive systems and waterfloods.
1000
Krw/Kro
100
10
1
0.1
0.01
20%
40%
60%
80%
Sw,%
Figure 10. log(Krw/Kro) vs. Sw.
Averaging
The next task is how to correctly average the data whether in a single well or for entire
field. The proper method of averaging depends on how permeabilities were distributed as
the rocks were deposited and how they were altered by secondary processes. The choice
of averaging technique can be significant.
If the permeable units extend laterally around the well, flow within strata may be treated
as if it is parallel and a thickness weighted arithmetic average is appropriate.
N
Ka 
K h
i 1
N
h
i 1
7
i i
i
,
(1)
where K a
interval i.
is the arithmetic average permeability and Ki is the permeability for the
For calculating flow in series, such as in the case of vertical flow, than the harmonic
average permeability should be used.
N
h
Kh 
i
i 1
N
hi

i 1 K i
,
(2)
where K h is the harmonic average permeability.
If permeabilities are randomly distributed, the geometric average (or log mean) applies.
K g  K1 K 2 ...K N 
1/ N
,
(3)
where K g is the geometric average permeability. Since permeability exhibits a lognormal distribution, the geometric mean is the appropriate value.
Following similar methods, average properties of porosity can be defined.
Arithmetic average porosity:
N
a 
 h
i 1
N
i i
,
h
i 1
(4)
i
where  a is the arithmetic average porosity and i is the porosity for interval i.
Harmonic average porosity:
N
h 
h
i 1
N
i
hi

i 1
,
(5)
i
where  h is the harmonic average porosity.
Geometric average porosity:
 g  1 2 ... N 1 / N ,
(6)
where  g is the geometric average porosity. Porosity typically is normal distributed,
therefore an arithmetic mean is correct.
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