International Journal of Geosciences, 2011, 2, 366-372
doi:10.4236/ijg.2011.23039 Published Online August 2011 (
Copyright © 2011 SciRes. IJG
Porosity a nd Li th ol og y Prediction in Eve F i e l d , N i g e r Delta
Using Compaction Curves and Rock Physics Models
M. T. Olowokere, J. S. Ojo
Department of Applied Geophysics, Federal University of Technology, Akure, Nigeria
Received January 8, 2011; revised May 10, 20 1 1; accepted June 12 , 20 1 1
The primary objective of this study is to investigate the porosity-depth trends of shales and sands and how
they affect lithologies. Compaction curves from well logs of five wells were determined using interval transit
time from sonic logs. The depth of investigation lies between 1087 m and 2500 m. Based on the shale and
sand trend modeling, the study intends to determine the model to be used for lithology prediction at various
depths given the interplay between shale and sand compaction. The improved understanding of the physical
properties of shales and sands as a function of burial depth was demonstrated, in conjunction with a good
understanding of how compaction affects lithology. The compaction curve for shale and sand lithologies var-
ies with shale being parabolic in form, and sands with linear and exponential in nature. Plots of sonic poros-
ity against depth show great dispersion in porosity values while plotting porosity values against depth for
different lithologies produced well-defined porosity trends. This shows decrease in porosity with depth. The
negative porosity trend is less marked in sandstones, and faster in shale which suggests that it is possible to
make accurate porosity predictions using compaction trend. The porosity trend showed exponential relation-
ship at small depth less than 2500m. The linear and exponential models are not dependable at large depth.
The result shows that the compaction models applicable for sandstones do not necessary apply for shales.
Keywords: Compaction Trend, Lithology, Porosity, Reservoir Characteristic, Velocity Logging, Sand–Shale
1. Introduction
Various authors have proposed different theoretical mo-
dels to show the fluid-solid interaction in reservoir rocks
for the purposes of both porosity and lithology pr ediction
and fluid substitution (e.g. Bjørlykke, 1998; and Ehren-
berg, 1990). However, these models can only be applied
under certain conditions because the theories have some
limitations. Athy, (1930); Magara, (1976a); Sclater and
Christie, (1980), Magara (1980) Liu and Roaldset (1994)
and Selley (1978)) have proposed a variety of models
that shows the relationship between porosity and depth.
Magara (1980) and Selley (1978) used linear porosity-
depth relationship to describe diagenetic changes affect-
ing compaction. A parabolic relationship has been pro-
posed by Liu and Roaldset (1994). Exponential curves
were probably first introduced by Athy (1930) to des-
cribe porosity in shale. Sclater and Christie (1980) used
exponential curves for porosities in sandstone, siltstone
and chalk in the Central Graben of the North Sea, but
their exponential curve for shale was criticized by Bald-
win and Hutler (1985) who proposed a power-law curve
for shale compaction (although they agreed with Sclater
and Christie’s sandstone compaction curve). Schmokker
and Halley (1982) proposed an exponential curve for
carbonate after their work in South Florida Basin. Using
exponential curves to describe normal compaction in
shale were also favoured by Korvin, (1984); Magara,
(1980); Selley, (1978 ); and Haung and Gradstein (1990 ).
Issler, 1992 used the time-average equation of Wyllie et
al. (1956) to estimate porosity and clay content in con-
solidated formations.
This paper is a unique study of porosity prediction in
the Niger Delta using compaction curves to improve re-
servoir modelling and production.
In this study, we derive the local shale and sand com-
paction trend for the Eve (gas-and-oil) field by inte-
grating rock physics modeling with well-log and seismic
data analysis. This model is based on the calibration of
core and well-log data. Based on the compaction trend
modeling, we demonstrate that improved understanding
of the physical properties of shales as a function of burial
depth, in conjunction with a good understanding of how
compaction affects rock properties of sandstones, will
improve our ability to characterize and predict porosity
and lithology of sandston e reservoirs embedded in shales.
Log information from nine wells within the study area
was used because they are representative of various types
of reservoir quality: well develo ped sands, marginal pro-
ducing sands, and non-producing units.
In order to predict porosity from a specific rock pro-
perty, prior to drilling, the particular rock property must
be known or predictable. Depth to a prospect is usually
well constrained, so a compaction curv e is easily app lied .
However, unless the compaction curve is based on local
data, the prediction uncertainty will commonly be too
large to be useful. An approach to porosity prediction
based on the compaction process and parameter, depth
that is usually pred ictable is used. This approach fo cuses
on compaction and cementation, which consider total
porosity loss with depth of burial. This in part accounts
for the wide range of porosity, at any given depth, in the
data sets. The depositional poro sity in shales is normally
much higher (60% - 80%) than in sands (about 40%), but
we expect a shallow crossover with depth due to the
mechanical collapse of the shales. The platy clay fabric
in the shales is more prone to compaction than the
assemblage of spherically shaped grains in sands; hence,
the more rapid mechanical porosity reduction in shales
than sands.
Linear and nonlinear regression are employed to de-
rive porosity, shale volumes, so that the effects of litho-
logy, fluid, temperature, pressure and other factors can
be compensated for. This method requires log data and
core data as inputs, and the outputs are reservoir para-
meters. The method was tested on field data from a num-
ber of reservoirs from Eve Field, Niger Delta and obtains
satisfactory results.
2. Location of the Study Area
Figure 2 shows the general characteristic of the refe-
rence field “Eve oil field” located southeast of Lagos,
Nigeria, just offshore at the western end of the Niger
Delta. Water depth is approximately 1 5 m.
3. Data and Method of Study
The study area, which covers about 25 km2 in the central
part of Eve Field, was selected for three main reasons: (1)
the good quality of the surface seismic, (Figure 3) (2) a
better definition of structural features, which is simpler
than the rest of the area, and (3) the well control repre-
sented by 11 wells, most of them with a complete set of
well logs.
3.1. Well Data
To date, 66 wells and 3 horizontal sidetracks have been
drill in the field since discovery encountering hydro-
Figure 1. Gamma-ray correlation for Eve wells A, B, and C.
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Figure 2. Location of Eve Field.
Figure 3. Seismic cross-section showing the main structural
features in Eve Field.
carbon between 2135 and 3811 m. There are thirteen
wells with Check-shot data.
3.2. Log Data
The field is covered with full suite of basic petrophysical
logs. All the wells have gamma ray and resistivity logs,
with about 65% coverage of SP log. Neutron logs have
about 80% well coverage, while the Density logs are
available in all the wells except well 12 and the 6 side-
tracks. The field has a modern vintage coverage of resis-
tivity logs ((LLD, LLS, MSFL). However, the old vin-
tage resistivity is available in Wells A. B and C have in-
duction resistivity in addition to the laterolog. Com-
pressional sonic was logged in 15 wells.
There is no Shear Sonic logged in the en tire field. The
log prints and digital data quality were checked and
judged to be of good quality and have been used for the
formation evaluation. To predict petrophysical properties
such as porosity, it is important to understand the effect
of depth on this variable. Compaction trend plays an im-
portant role in helping to establish the systematic rela-
tionships. The compaction trends were used to find sta-
tistical relations between depth and log properties.
3.3. Well-Log Signature, Correlation, and
Petrophysical Analysis
The recorded suite of logs can be grouped into two
categories: properties that affect seismic-wave propaga-
tion in the subsurface (e.g., compressional- and shear-
velocity logs (m/s), and density log (g/cc)) and properties
of interest for reservoir description but which do not
directly affect seismic-wave propagation (e.g. porosity
(%), water saturation (%), and clay content).
Petrophysical analysis through conventional cross-plots
is the key to relating the two grou ps. Logs were carefully
edited to compensate for washouts, cycle skipping, envi-
ronmental corrections, and any other problems. Selected
logs from Eve wells (Figure 4(a) and 4(b)) show the re-
sponses from different lithologies of Agbada Formation.
4. Calculating Porosity
Evaluation para meters, matrix density (ma) of 2.65 g/cc
and cementation exponent (m) of 1.6 were obtained from
core analyses while a saturation exponent (n) value of
1.8 was assumed in the absence of core measured data.
The formation water resistivity (Rw) values were ob-
tained from clean water bearing zones using the Pickett
plot method. The Porosity was calculated from density
logs using equation -
ma b
ma fl
= porosity
ma = Matrix grain density
fl = Density of pore fl ui d
b = Formation bulk density
Calculated porosity values range between 25% and 31%.
5. Rock-Physics Trends and Crossplot
To understand the governing compaction laws for the
Agbada Formation in Eve Field, there is need to in-
vestigate the porosity-velocity relationship and to place
the data points in a frame of reference. Th i s f r ame will be
sandstones from an active oil company’s database named
Proprietary’s model which includes more than 70 sam-
ples of sandstones with varying porosity and clay content
analyzed at an effective pressure of 40 Mpa, similar to
the pressure of the reservoir in the field.
Velocity versus porosity in selected wells was cross-
plotted to see how the well-log data fall within the
framework of the reference trends.
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Figure 4. (a): Well logs from Eve well A showing the response from 2.00 level; (b): Well logs from Eve well B showing the
response from other reservoir levels.
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6. Discussion of Results
In Figure 5, we crossplot velocity and density-porosity
for Eve wells A, B and C individually and combined.
Because the caliper indicates good borehole condition
through most of the section, the data are not filtered to re-
move effects for washed-out intervals. From this cross-
plot we can see that there is a good correlation between
Proprietary data and the Eve data. There is a better match
between Proprietary model and the Eve data where the
sands are clean. The data points for rocks with porosities
between 0 and 5% fall outside Proprietary model and
these lithologies exhibit the highest scattering in the
cross-plots. There are two main trends shown in the
cross-plot, one for lithologies with velocities higher than
about 1372 m/s and other for lithologies with velocities
lower than 1372 m/s. The first class of lithology corre-
sponds to shaly-sands that are partly represented by Pro-
prietary database when the clay content is between 18
and 50%. The second class is associated with shales. The
high scattering of these rocks can likely be related to the
high clay content, which affects the compaction of the
rock, the pore geometry and the aspect ratio.
The main reservoir in the field is associated with rocks
with porosities that are higher than 10%. Th ese rocks fall
between Proprietary zero-clay reference line and the re-
ference line for clay contents between 12 and 17%. These
rocks are composed of clean sands that have velocities
ranging from 1220 to 1372 m/s.
Rock physics depth trends for shales (blue) and sands
(red), juxtaposed on well-log data from Well B pene-
trating D levels are shown in Figure 6. For a better illu-
stration of low porosity points (i.e., for shales) the den-
sity-porosity for this example is calculated using sand-
stone as the density of the matrix (2.65 g/cc), which ex-
plains why the porosity looks overestimated. We can see
correlation between velocity and gamma ray, and velo-
city and porosity.
Moreover, porosity correlates with gamma ray—the
smaller gamma ray the larger porosity. Green color co-
rresponds to low shale content while purple color indi-
cates high shale content.
The observed velocity-porosity-shale dependence su-
pports the fact that the Proprietary type rock physics
model can explain the behavior of the velocity porosity
relation in the field. In th is model, velocity is affected b y
porosity and clay content, porosity being the main factor.
For the whole section of Agbada, velocity is a good
indicator of lithology. Thus, velocities higher than 1372
m/s are most likely associated with sandstones, while
velocities lower than 137 2 m/s are most likely associated
with shales and siltston es.
The plot between Vp/Vs and acoustic impedance colour
Figure 5. C rossplot of d ensity and s oni c trans ittime a t wells A .
coded with shale volume juxtaposed on depth trend
models shows a parabolic relationship determining the
pore fluid types (Figure 7). Finally, in order to un-
derstand the finer details of the hydrocarbon-bearing
zones, rock physics crossplots of well-log data coloured
in terms hydrocarbon saturation juxtaposed on depth
trend models for shale (blue), brine sands (red), oil sands
(red), and gas sands (yellow) were generated and as
shown in Figure 8..
These plots (Figures 7 and 8) indicate that sand, and
shale facies are clearly separable in the smaller interval
(1.0 - 2.0 unit) and that effective porosity plays a crucial
role in hydrocarbon accumulation in this area. juxtaposed
on depth trend models for shale (blue), brine sands (red),
oil sands (red), and gas sands (yellow).
7. Conclusions
This study showed that velocity-por osity and impedance-
porosity relation for Agbada Formation in Eve Field is
nonlinear. Three main trends are recognized: one for
clean sands of the stratigraphic units, one for dirty sands
and other for shales units. The plots between Vp/Vs and
acoustic impedance colour coded with shale volume jux-
taposed on depth trend models shows a parabolic rela-
tionship. These plots indicate that sand, and shale facies
are clearly separable within a small reservoir interval.
A change in compaction trend from depth-velocity
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Figure 6. Rock physics depth tr ends for shales (blue) and sands (red), juxtaposed on well-log data from Well B penetrating D
Figure 7. Rock physics crossplots of well-log data colored in
terms of shale volume, juxtaposed on depth trend models
for shale (blue), brine sands (red), oil sands (brown), and
gas sands (yellow).
crossplot is related to changes in porosity in Eve Field.
Compaction trends are nonlinear and have proved to be a
powerful tool to predict porosity and gamma ray curves,
from interval transit time measured from sonic log.
This study also showed that a systematic approach
com-bined with determination of compaction trend tech-
Figure 8. Rock physics crossplots of well-log data coloured
in terms hydrocarbon saturation.
niques helped in understanding the subsurface, and allowed
precise mapping of lithology. Predicting effective poro-
sity through d etermination of compactio n trend s has pro-
vided a very high degree of confidence in analyzing the
porous and nonporous zones of the reservoir. This study
has been very helpful in providing more meaningful geo-
logic information about the extent, shape and lateral
lithological var i at i on of reser v o i rs.
8. Acknowledgements
We would like to acknowledge Department of Petroleum
Resources (DPR) for supplying the data used for the
study. We sincerely appreciate our colleagues for con-
structive reviews and two other anonymous referees.
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