# Subsurface and Petrophysical Studies of Shaly-Sand Reservoir Targets in Apete Field, Niger Delta

Authors: P. A. Alao; A. I. Ata; C. E. Nwoke
Journal: ISRN Geophysics
 (2013)

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## Abstract

Conventional departures from Archie conditions for petrophysical attribute delineation include shaliness, fresh formation waters, thin-bed reservoirs, and combinations of these cases. If these departures are unrecognized, water saturation can be overestimated, and this can result in loss of opportunity. Wireline logs of four (4) wells from Apete field were studied to delineate petrophysical attributes of shaly-sand reservoirs in the field. Shale volume and porosities were calculated, water saturations were determined by the dual water model, and net pay was estimated using field-specific pay criteria. Ten sand units within the Agbada formation penetrated by the wells were delineated and correlated and their continuity was observed across the studied wells. The reservoirs had high volume of shale (Vcl), high hydrocarbon saturation, low water saturation, and good effective porosity ranging  12.50–46.90%, 54.00–98.39%, 1.61–46.0%, and 10.40–26.80%, respectively. The pay zones are relatively inhomogeneous reservoirs as revealed from the buckle’s plot except in Apete 05. The direction of deposition of the sands was thus inferred to be east west. Empirical relationships apply with variable levels of accuracy with observation of the porosity-depth, water saturation-depth, and water saturation-porosity trends. Core data is recommended for better characterization of these reservoirs.


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## Body

## 1. Introduction

Shales can cause complications for the petrophysicist because they are generally conductive and may therefore mask the high resistance characteristic of hydrocarbons [1]. Several factors are to be considered when delineating petrophysical attributes for shaly-sand reservoirs because clay minerals add conductivity to the formation especially at low water saturations.Clay minerals attract water that is adsorbed onto the surface, as well as cations (e.g., sodium) that are themselves surrounded by hydration water. This gives rise to an excess conductivity compared with rock, in which clay crystals are not present, and this space might otherwise be filled with hydrocarbon. Using Archie’s equation in shaly sands results in very high water saturation values and may lead to potentially hydrocarbon bearing zones being missed. Moreover, in clean sands, the irreducible water volume is a function of the surface area of the sand grains and therefore the grain size, but for shaly sands the addition of silt and clay usually decreases effective porosity due to poorer sorting and increases the irreducible water volume with the finer grain size [2]. Archie’s equation was developed for clean rocks, and it does not account for the extra conductivity caused by the clay present in shaly sands. Therefore, Archie’s equation would not provide accurate water saturation in shaly sands. In fact, water saturations obtained from Archie’s equation have a tendency to overestimate the water in shaly sands. Several models have been proposed by many researchers for shaly-sand analysis such as Juhasz model, dual water model, Indonesian model, Waxman and Smits model, and so forth.


## 2. Synopsis of the Geology

The stratigraphic sequence of the Niger Delta comprises three broad lithostratigraphic units, namely,(1) a continental shallow massive sand sequence, the Benin Formation, (2) a coastal marine sequence of alternating sands and shales, the Agbada Formation, and (3) a basal marine shale unit, the Akata Formation(Figure 2). Outcrops of these units are exposed at various localities (Figure 1). The Akata Formation consists of clays and shales with minor sand intercalations. The sediments were deposited in prodelta environments. Petroleum in the Niger Delta is produced from these unconsolidated sands in the Agbada Formation. Characteristics of the reservoirs in the Agbada Formation are controlled by depositional environment and by depth of burial. The sand percentage here is generally less than 30%. The Agbada Formation consists of alternating sand and shales representing sediments of the transitional environment comprising the lower delta plain (mangrove swamps, floodplain, and marsh) and the coastal barrier and fluviomarine realms. The sand percentage within the Agbada Formation varies from 30 to 70%, which results from the large number of depositional offlap cycles [3]. A complete cycle generally consists of thin fossiliferous transgressive marine sand, followed by an offlap sequence which commences with marine shale and continues with laminated fluviomarine sediments followed by barriers and/or fluviatile sediments terminated by another transgression [4, 5].Figure 1
Map of Southern Nigeria showing outcrops of cretaceous and tertiary formations and type localities of subsurface stratigraphic units. After Short and Stauble [6].Figure 2
Stratigraphic column of the Niger Delta. After Shannon et al. [7] and Doust et al. [8].The Benin Formation is characterized by high sand percentage (70–100%) and forms the top layer of the Niger Delta depositional sequence. The massive sands were deposited in continental environment comprising the fluvial realms (braided and meandering systems) of the upper delta plain. The Niger Delta time-stratigraphy is based on biochronological interpretations of fossil spores, foraminifera, and calcareous nonnoplankton. The current delta-wide stratigraphic framework is largely based on palynological zonations labeled with Shell’s alphanumeric codes (e.g., P630, P780, and P860). This allows correlation across all facies types from continental (Benin) to open marine (Akata). There have been concerted efforts, within the work scope of the stratigraphic committee of the Niger Delta (STRATCOM), to produce a generally acceptable delta-wide biostratigraphic framework [9] but not much again has been accomplished after several data gathering exercise by the committee. The sediments of the Niger Delta span a period of 54.6 million years during which, worldwide, some thirty-nine eustatic sea level rises have been recognized [10]. Correlation with the chart of Galloway [11] confirms the presence of nineteen of such named marine flooding surfaces in the Niger Delta. Eight of these are locally developed. Adesida et al. [10] defined eleven lithological mega sequences marked at the base by regional mappable transgressive shales (shale markers) that are traceable across depobelt boundary faults and proposed these as the genetic sequences that can be used as the basis for lithostratigraphy of the Niger Delta.


## 3. Methodology

Composite wireline log data from four well logs were interpreted. The basic analysis procedure used involves the following steps; each of which is described in the following sections.


### 3.1. Import and Well Log Data

The well data was imported into the software used and well log correlation (Figure3) was done after which the petrophysical attributes were delineated. Well correlation helped in determining the direction of thickness of sand being mapped and the lateral continuity of these reservoirs.Figure 3
Well correlation of all reservoir sands.


### 3.2. Zoning and Point Selection

Zoning is of vital importance in the interpretation of well logs. The logs were split into potential reservoir zones and nonreservoir zones. Hydrocarbon bearing intervals were identified and differentiated based largely on the readings from the shallow and deep reading resistivity tools. However, hydrocarbon typing (oil and gas differentiation) was based on density-neutron logs overlay.


### 3.3. Compute Shale Volume from the Gamma Ray

This was derived from the gamma ray log first by determining the gamma ray indexIGR [12]:
(1)IGR=(GRlog-GRmin)(GRmax-GRmin),
where IGR = gamma ray index; GRlog = gamma ray reading of the formation; GRmin = minimum gamma ray reading (sand baseline); GRmax = maximum gamma ray reading (shale baseline).For the purpose of this research work, Larionov’s [13] volume of shale formula for tertiary rocks was used:
(2)Vsh=0.083(23.7*IGR-1)Vsh: volume of shale and IGR: gamma ray index.


### 3.4. Compute Total Porosity and Shale-Corrected (Effective) Porosity

Total and effective porosity was estimated from the density, neutron, and sonic logs using Archie’s equation.


### 3.5. Compute Water Saturation

Water saturation was estimated using Archie’s water saturation formula and Schlumberger’s dual water model.


### 3.6. Estimate Net Pay

Calculate net pay using field-specific net pay cutoffs. Cutoff criteria used are water saturation < 50%, porosity > 10%, and volume of clay < 50%.


### 3.7. Use of Crossplots

Pickett, buckles, and neutron-density crossplots were generated to understand reservoir properties. Pickett plot which is a resistivity-porosity plot generated was used to determine saturation values alongside Archie parametersa and m. Porosity is calculated from the neutron porosity and density porosity logs and is plotted against the resistivity data obtained from the deep resistivity log. Porosity is plotted on the y-axis with a logarithmic scale ranging from 0.1% to 100%, while the resistivity is plotted on the x-axis with a logarithmic scale ranging from 1 to 100 ohm meter. In order to properly characterize the reservoir sands delineated and correlated across the studied wells, Buckles plot, a plot of Sw versus Φ, was generated to depict whether or not the sands are at irreducible water saturation. Porosity is plotted on the y-axis with a scale ranging from 0 to 40% porosity (shown in decimals), while water saturation is plotted on the x-axis with a scale ranging from 0 to 100% (shown in decimals) water saturation. The scale for bulk volume water lines (grey lines) ranges from 0.01 to 0.25 and is shown as a secondary y-axis. The implicit assumption in the Buckles plot approach is that the product of irreducible water saturation and porosity is constant. Empirical relationships were also established for porosity-depth, water saturation-depth, water saturation-porosity, and permeability-depth to check the trends.


## 3.1. Import and Well Log Data

The well data was imported into the software used and well log correlation (Figure3) was done after which the petrophysical attributes were delineated. Well correlation helped in determining the direction of thickness of sand being mapped and the lateral continuity of these reservoirs.Figure 3
Well correlation of all reservoir sands.


## 3.2. Zoning and Point Selection

Zoning is of vital importance in the interpretation of well logs. The logs were split into potential reservoir zones and nonreservoir zones. Hydrocarbon bearing intervals were identified and differentiated based largely on the readings from the shallow and deep reading resistivity tools. However, hydrocarbon typing (oil and gas differentiation) was based on density-neutron logs overlay.


## 3.3. Compute Shale Volume from the Gamma Ray

This was derived from the gamma ray log first by determining the gamma ray indexIGR [12]:
(1)IGR=(GRlog-GRmin)(GRmax-GRmin),
where IGR = gamma ray index; GRlog = gamma ray reading of the formation; GRmin = minimum gamma ray reading (sand baseline); GRmax = maximum gamma ray reading (shale baseline).For the purpose of this research work, Larionov’s [13] volume of shale formula for tertiary rocks was used:
(2)Vsh=0.083(23.7*IGR-1)Vsh: volume of shale and IGR: gamma ray index.


## 3.4. Compute Total Porosity and Shale-Corrected (Effective) Porosity

Total and effective porosity was estimated from the density, neutron, and sonic logs using Archie’s equation.


## 3.5. Compute Water Saturation

Water saturation was estimated using Archie’s water saturation formula and Schlumberger’s dual water model.


## 3.6. Estimate Net Pay

Calculate net pay using field-specific net pay cutoffs. Cutoff criteria used are water saturation < 50%, porosity > 10%, and volume of clay < 50%.


## 3.7. Use of Crossplots

Pickett, buckles, and neutron-density crossplots were generated to understand reservoir properties. Pickett plot which is a resistivity-porosity plot generated was used to determine saturation values alongside Archie parametersa and m. Porosity is calculated from the neutron porosity and density porosity logs and is plotted against the resistivity data obtained from the deep resistivity log. Porosity is plotted on the y-axis with a logarithmic scale ranging from 0.1% to 100%, while the resistivity is plotted on the x-axis with a logarithmic scale ranging from 1 to 100 ohm meter. In order to properly characterize the reservoir sands delineated and correlated across the studied wells, Buckles plot, a plot of Sw versus Φ, was generated to depict whether or not the sands are at irreducible water saturation. Porosity is plotted on the y-axis with a scale ranging from 0 to 40% porosity (shown in decimals), while water saturation is plotted on the x-axis with a scale ranging from 0 to 100% (shown in decimals) water saturation. The scale for bulk volume water lines (grey lines) ranges from 0.01 to 0.25 and is shown as a secondary y-axis. The implicit assumption in the Buckles plot approach is that the product of irreducible water saturation and porosity is constant. Empirical relationships were also established for porosity-depth, water saturation-depth, water saturation-porosity, and permeability-depth to check the trends.


## 4. Results and Discussions

Petrophysical attributes as porosity (effective and total), reservoir thickness (net and gross), water saturation (Archie and dual water model), and volume of shale (Tables1, 2, 3, 4, 5, 6, and 7) were delineated in this research work. Results from petrophysical analysis revealed reservoir Sand F to be the most viable reservoir with net thickness as high as 126.50 ft. All the ten reservoirs exhibited good petrophysical attributes with high porosity and hydrocarbon saturation. The sands are shaly sands with high volume of shale resulting in overestimated water saturation in the reservoirs; dual water model was used for estimating the water saturation for better appraisal of the reservoirs due to their shaly nature.Table 1
Average total porosity for all reservoir sands.
Reservoir
Apete 05
Apete 06
Apete 15
Apete 16
A
—
0.254
—
0.250
B
0.223
0.225
—
0.224
C
0.280
0.234
—
—
D
—
0.196
0.174
0.122
E
0.214
0.230
—
0.104
F
0.272
0.248
0.239
0.154
G
—
0.232
—
0.178
H
0.210
0.177
—
0.129
I
—
0.204
—
0.141
J
—
0.126
0.172
—
Blank spaces mean the zone is not a reservoir.Table 2
Average effective porosity for all reservoir sands.
Reservoir
Apete 05
Apete 06
Apete 15
Apete 16
A
—
0.247
—
0.218
B
0.219
0.218
—
0.203
C
0.259
0.214
—
—
D
—
0.167
0.158
0.111
E
0.201
0.207
—
0.101
F
0.250
0.221
0.193
0.145
G
—
0.211
—
0.169
H
0.205
0.160
—
0.113
I
—
0.200
—
0.137
J
—
0.123
0.167
—
Blank spaces mean the zone is not a reservoir.Table 3
Average net-to-gross ratio for all reservoir sands.
Reservoir
Apete 05
Apete 06
Apete 15
Apete 16
A
—
0.793
—
0.826
B
0.857
0.771
—
0.851
C
0.515
0.933
—
—
D
—
0.712
0.622
0.449
E
0.654
0.615
—
0.125
F
0.797
0.931
0.886
0.785
G
—
0.981
—
0.692
H
0.389
0.278
—
0.289
I
—
0.853
—
0.654
J
—
0.328
0.279
—
Blank spaces mean the zone is not a reservoir.Table 4
Average volume of shale for all reservoir sands.
Reservoir
Apete 05
Apete 06
Apete 15
Apete 16
A
—
0.217
—
0.308
B
0.292
0.339
—
0.243
C
0.162
0.122
—
—
D
—
0.363
0.290
0.350
E
0.415
0.346
—
0.274
F
0.245
0.192
0.288
0.312
G
—
0.162
—
0.276
H
0.327
0.360
—
0.288
I
—
0.258
—
0.279
J
—
0.469
0.357
—
Blank spaces mean the zone is not a reservoir.Table 5
Average water saturation from dual water model for all reservoir sands.
Reservoir
Apete 05
Apete 06
Apete 15
Apete 16
A
—
0.333
—
0.238
B
0.071
0.298
—
0.106
C
0.200
0.436
—
—
D
—
0.118
0.169
0.231
E
0.223
0.002
—
0.046
F
0.353
0.071
0.164
0.246
G
—
0.460
—
0.176
H
0.107
0.023
—
0.079
I
—
0.017
—
0.087
J
—
0.011
0.016
—
Blank spaces mean the zone is not a reservoir.Table 6
Average water saturation from Archie’s model for all reservoir sands.
Reservoir
Apete 05
Apete 06
Apete 15
Apete 16
A
—
0.377
—
0.324
B
0.164
0.406
—
0.189
C
0.221
0.449
—
—
D
—
0.241
0.232
0.384
E
0.355
0.115
—
0.140
F
0.417
0.113
0.257
0.339
G
—
0.492
—
0.244
H
0.192
0.151
—
0.157
I
—
0.072
—
0.177
J
—
0.229
0.135
—Table 7
Net thickness (ft) for all reservoir sands.
Reservoir
Apete 05
Apete 06
Apete 15
Apete 16
A
—
78.50
—
57.00
B
12.00
45.50
—
31.50
C
34.00
42.00
—
—
D
—
89.00
102.00
62.00
E
78.50
40.00
—
13.00
F
169.50
95.00
156.00
67.50
G
—
126.50
—
45.00
H
27.50
22.00
—
11.00
I
—
81.00
—
70.00
J
—
20.00
12.00
—


### 4.1. Crossplot

Correlation analysis was performed to determine whether the petrophysical attributes (water saturation and porosity) are interdependent. Generally, the effective porosity decreases with depth (Figure4) with high correlation coefficient except in Apete 15 where there was increase in porosity with depth. The observed reduction in depth would likely be due to the effect of compaction resulting from overburden pressure. Water saturation generally increases with depth (Figure 5) except in Apete 16. This implies that reservoirs in Apete field occur in shallow depth hence the unavailability of reservoirs as we go deeper into the subsurface. Efforts made to delineate trends for water saturation and effective porosity were marginally efficient. From crossplot results (Figure 6), effective porosity reduces with an increase in water saturation in Apete 05 and 06. Specifically, in Apete 15 and 16, there was no correlation at all, as the correlation coefficient was extremely low which suggests that no relationship exists between the two petrophysical attributes. Buckles plot for the reservoirs in the wells were generated (Figure 7); the results from these plots reveal only Apete 06 to be at irreducible water saturation as the data points align along the bulk volume of water (BVW) trend line due to the consistency of the data points. The reservoirs zones in this well are considered to be homogenous; therefore, hydrocarbon production from Apete 06 should be water free [14]; that is, the reservoirs would have a low water cut.Figure 4
Porosity-depth plot for Apete 05, 06, 15, and 16.Figure 5
Water saturation-depth plot for Apete 05, 06, 15, and 16.Figure 6
Effective porosity—water saturation trend for Apete 05, 06, 15, and 16.Figure 7
Buckles plot for Apete 05, 06, and 15.Pickett plot (Figure8) reveals the reservoirs to be somewhat shaly which is observed with saturation exponent being less than 2 in the best porosity type. This is further confirmed from the Neutron-Density crossplot (Figure 9). Pickett plot was used to determine Archie parameters, tortuosity (a), and cementation exponent (m) which is approximately 1 and 2, respectively. Neutron density crossplot reveals more of laminated clay in the reservoirs; this should be taken into consideration during well planning. The shale morphology generally changed from laminated to dispersed which affects saturation mixing function hence the need to use another saturation model (Schlumberger’s dual water model) which is designed specifically for shaly sands, rather than the conventional Archie’s water saturation model. Results from both water saturation models used show a wide disparity which could not have been noticed if only the conventional Archie’s model was used which could have led to bypassing some reservoirs as well as undervaluating the reserves in this field.Figure 8
Pickett plot of Sand C in Apete 05.Figure 9
Neutron-density crossplot for Apete 05 and 06.


## 4.1. Crossplot

Correlation analysis was performed to determine whether the petrophysical attributes (water saturation and porosity) are interdependent. Generally, the effective porosity decreases with depth (Figure4) with high correlation coefficient except in Apete 15 where there was increase in porosity with depth. The observed reduction in depth would likely be due to the effect of compaction resulting from overburden pressure. Water saturation generally increases with depth (Figure 5) except in Apete 16. This implies that reservoirs in Apete field occur in shallow depth hence the unavailability of reservoirs as we go deeper into the subsurface. Efforts made to delineate trends for water saturation and effective porosity were marginally efficient. From crossplot results (Figure 6), effective porosity reduces with an increase in water saturation in Apete 05 and 06. Specifically, in Apete 15 and 16, there was no correlation at all, as the correlation coefficient was extremely low which suggests that no relationship exists between the two petrophysical attributes. Buckles plot for the reservoirs in the wells were generated (Figure 7); the results from these plots reveal only Apete 06 to be at irreducible water saturation as the data points align along the bulk volume of water (BVW) trend line due to the consistency of the data points. The reservoirs zones in this well are considered to be homogenous; therefore, hydrocarbon production from Apete 06 should be water free [14]; that is, the reservoirs would have a low water cut.Figure 4
Porosity-depth plot for Apete 05, 06, 15, and 16.Figure 5
Water saturation-depth plot for Apete 05, 06, 15, and 16.Figure 6
Effective porosity—water saturation trend for Apete 05, 06, 15, and 16.Figure 7
Buckles plot for Apete 05, 06, and 15.Pickett plot (Figure8) reveals the reservoirs to be somewhat shaly which is observed with saturation exponent being less than 2 in the best porosity type. This is further confirmed from the Neutron-Density crossplot (Figure 9). Pickett plot was used to determine Archie parameters, tortuosity (a), and cementation exponent (m) which is approximately 1 and 2, respectively. Neutron density crossplot reveals more of laminated clay in the reservoirs; this should be taken into consideration during well planning. The shale morphology generally changed from laminated to dispersed which affects saturation mixing function hence the need to use another saturation model (Schlumberger’s dual water model) which is designed specifically for shaly sands, rather than the conventional Archie’s water saturation model. Results from both water saturation models used show a wide disparity which could not have been noticed if only the conventional Archie’s model was used which could have led to bypassing some reservoirs as well as undervaluating the reserves in this field.Figure 8
Pickett plot of Sand C in Apete 05.Figure 9
Neutron-density crossplot for Apete 05 and 06.


## 5. Conclusion

In the study of well logs from the Apete field, Niger Delta, it was observed that Apete 06 is the most economic well drilled in this field; apart from having the most presence of reservoirs it also has the highest net thickness of 639.50 ft (Table7). Generally, water saturation increases with depth and porosity reduces with depth as a result of compaction. Water saturation-porosity trends cannot be emphatically established except in Apete 05 and 06 where porosity reduces with increase in water saturation. The reservoirs are shaly sands with the shales mostly occurring as laminated clays which could act as impediment to flow during production and therefore causing reservoir compartmentalization. Shale morphology changes from laminated to dispersed, thereby affecting saturation mixing functions.


## Glossary

Buckle’s plot: A plot of water saturation(Sw) against porosity (Φ) generated to depict whether or not the sands are at irreducible water saturation (Φ on y-axis and Sw on the x-axis)
Bulk volume of water (BVW): Percentage of the total rock volume occupied by water
Core data: Set of data derived from analysis of core (rock) samples
Eustatic sea level: Sea level change which occurs on a global scale
Fluviatile: Pertaining or relating to rivers, found in or near rivers
Fossiliferous: Means containing fossils
Laminated: Composed of layers bonded together
Marine flooding surface: A surface of deposition at the time the shoreline is at its highest landward position
Morphology: Means this refers to the description of the shape of geologic features
Offlap: The arrangement of strata deposited on the sea floor during the progressive withdrawal of the sea from land
Pickett plot: Plot of Archie’s saturation parameters against resistivity of water so as to estimate the water saturation in such a reservoir
Transgression: Progressive movement of the sea towards land
Transitional environment: Environment situated between the continental realm and the marine
Water cut: Amount of water produced with oil.



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*Source: 102450-2013-12-10.xml*