Internationa l Journal of Geosciences, 2014, 5, 93-106
Published Online January 2014 (
Static Reservoir Modeling Using Well Log and 3-D Seismic
Data in a KN Field, Offshore Niger Delta, Nigeria
Lukumon Adeoti1, Njoku Onyekachi1, Ola wale O la t in su 2, Julius Fatoba3, Musa Bello1
1Department of Geosciences, Univer sity of Lago s , Lagos, Nigeria
2Department of Physics, Universi ty of Lagos, Lagos, Nigeria
3Department of Earth Science, Olab isi Onab anjo University, A go-Iwoye, Niger ia
Received November 22, 2013; revised December 23, 20 13; accepted January 18, 2014
Copyright © 2014 Lukumon Adeoti et al. This is an open access art icle d ist ribu ted under the Creat ive Co mmons Attri bu tio n License,
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This study f ocuses on t he applica tion of 3D static model using 3-D seismic and well log data for proper optimiza-
tion and development of hydrocarbon potential in KN field of Niger Delta Province. 3D Seismic data were used
to generate the input interpreted horizon grids and fault polygons. The horizon which cut across the six wells
was used for the analysis and detailed petrophysical analysis was carried out. Structural and pro perty modeli ng
(net to gross, porosity, permeability, water saturation and facies) were distributed stochastically within the con-
structed 3D grid using Sequential Gaussian Simulation and Sequential Indicator Simulation algorithms. The
reservoir structural model show system of different oriented growth faults F1 to F6. Faults 1 and Fault 4 are the
major growth faults, dipping towards south-west and are quite extensive. A rollover anticline formed as a result
of deformation of the sediments deposited on the downthrown block of fault F1. The other faults (2, 3, 5 and 6)
are minor fault (synthetic and antithetic). The trapping mechanis m is a fault assisted anticlinal closure. Results
from well log analysis and petrophysical models classified sand 9 reservoir as a moderate to good reservoir in
terms of facies, with good porosity, permeability, moderate net to gross and low water saturation. The volume-
tric calculation of modeled sand 9 horizon reveals that the (STOIIP) value at the Downthrown and Ramp seg-
ment are 15.7 MMbbl a nd 3.8 MM bbl respe ctively . This i mplies that the mapped horiz on indicat es hydrocarbon
accumulation in economic quantity. This study has also demonstrated the effectiveness of 3-D static modeling
technique as a tool for better understanding of spatial distribution of discrete and continuous reservoir proper-
ties, hence, has provided a framework for future prediction of reservoir performance and production behavior of
sand 9 reservoir. However, more horizontal wells should be drilled to enhance optimization of the reservoir.
Porosity; Permeability; Water Saturation; Volumetric Calculation; Facies
1. Introduction
The increasing demand for petroleum products has posed
a change to the search of oil and gas. This search for hy-
drocarbon has developed increase with advances in
greater computational technology to evaluate the proba-
bility of hydrocarbon proneness of the basin thereby li-
miting the risk factor associated with hydrocarbon. Gov-
ernment revenue of most oil producing countries in the
world depends on these products. In Nigeria, Niger Delta
province has commercial accumulation of oil and gas.
The production of oil and gas is from the accumulation in
the pore spaces of reservoir rock usually sandstone, li-
mestone or dolomite. In Niger Delta, petroleum produc-
tion is produced in sandstone and unconsolidated sand of
Agbada formation. This formation is characterized by
alternating sandstones and shale with rock units varying
in thi ckness from 1 00 ft to 15000 ft [1]. The sand in t his
formation is mainly hydrocarbon reservoir with shale
providing lateral and vertical seals [2]. The Niger Delta
is situated in the Gulf of Guinea between the longitude
5˚E - 8˚E and Latitude 3˚N - 6˚N. The Niger Delta lo-
cated chiefly onshore and offshore of Nigeria, covers
approximately 105,000 km squa re .
As Oil and gas deposits are found in the porous forma-
tions i n sed imentar y basins, under normal conditions, the
reservoir occurs at locations where the appropriate por-
ous formation is at a higher elevation than the surround-
ing region. The task of the interpreter is to locate such
occurrences. T he means of doing this is the proper inter-
pretation of seismic data recorded for region of interest.
As with any ph ysical procedure of this nature, it becomes
highly desirable to simulate the data collection process
and to gain insight by the examination of known situa-
tions [3]. Realistic 3D geological models are then re-
quired as input to reservoir simulation programs which
predict the movement of rocks under various hydrocar-
bon scenarios. An actual reservoir can only be developed
and produced once and mistakes can be tragic and
wasteful. It is essential to model the reservoir as accu-
rately as possible in order to calculate the reserves and to
determine the most effective way of recovering as much
of the petroleum economically as possible [4-7], hence,
allows for 3D visualization of the subsurface, which en-
hances understanding of reservoir heterogeneities and
helps to improve recovery rates, as low recovery rates
stem from inefficient sweep caused by poor knowledge
of inter well-scale heterogeneities [8].
The advances in computational technology, modern
reservoir models can accommodate increasingly detailed
3D data that illu strate the spat ial distribution of reservoir
properties. Subsurface reservoir characterization typical-
ly incorpora tes well data augmented with seismic data to
establish the geological model of the reservoir [8]. [9]
worked on 3D integr ated s tatic modeli ng usi ng geost atis-
tical methods in Asmari reservoir, Marun oil field, Iran.
In this study, he predicted future reservoir performance
and production history. [10] worked on detailed facies
definition and 3D static model: Reser voir manage ment o f
the Eocene producing units in Block X of the Talara Ba-
sin in Northwest Peru. Here, he constructed a reliable
stratigraphic framework in the identification of specific
association stratigraphic or depositional surfaces, defin-
ing genetic units. Zones of po tential reservoir connectiv-
ity were built.
Applying a reservoir modeling tool effectively is af-
fected by the integrity of the data used and an under-
standing of the reservoir with the lithology of its host
rock. The KN field in the Niger Delta had previously
been difficult to describe owing to its problem of poor
data quality as a result of the 2D seismic data that was
used in the interpretation process, hence when wells were
drilled in the field the reservoir was missed. The study
was carried out to overcome this problem by incorporat-
ing 3D seismic data interpretation and well data to de-
velop 3D static model that would make the data more
2. Geology of the Study Area
Niger Delta is a prolific hydrocarbon belt in the world.
The formation of Niger Delta basin was initiated in the
early Tertiary time. The Niger Delta is situated in the
Gulf of Guinea and extends throughout the Niger Delta
province. From the Eocene to the present, the Delta has
prograded Southwest ward, forming depobelts that
represent the most active portion of the Delta at each
stage of its development [11].
Deposition of the three formations occurred in each of
the five off lapping silicicla stic sedimentation cycles that
comprise the Niger Delta. These cycles (depobelts) are
30 - 60 kilometers wide, prograde southwestward 250
kilometers over oceanic coast into the gulf of guinea, and
are defined by synsedimentary faulting that occurred in
response to variable rates of subsidence and sediment
supply [11]. The interplay of subsidence and supply rates
resulted in deposition of discrete depobelts when further
crustal subsidence of the basin could no longer be ac-
commodated, the focus of sediment deposition shifted
seaward, forming a new depobelt. Each depobelt is a
separate unit that corresponds to a break in regional dip
of the delta and is bounded landwar d by growth faults and
seaward by large counter-regional faults or the growth
fault of the next seaward belt [11]. Five major depobelts
are generally recognized, each with its own sedimentation,
deformation and petroleum history (Figure 1).
The sedimentary wedge of the Niger Delta contains a
major submarine part [13] which forms part of the com-
plex c o nti ne nta l mar gi n int r uding into t he Gu lf of Gu ine a.
In the Niger delta province, the Tertiary Niger delta
(Akata-Agbada) petroleum system has been identified.
The Delta formed at the site of a rift triple junction re-
lated to the opening of the Southern Atlantic starting in
the late Jurassic from interbedded marine shale of the
lowermost Agbada formation and continuing into the
cretaceous. The Delta proper began developing in the
Eocene, accumulating sediments that now are over 10km
thick. T he primary source rock is the upper Akata forma-
tion, the marine-shale facies of the Delta, with possibly
contribution from interbedded marine shale of the lo-
wermost Agbada formation. Oil is produced from sand-
stone facies within the Agbada Formation, ho wever, tur-
bidite sand in the upper Akata Formation is a potential
target in deep water offshore and possibly beneath cur-
rently producing intervals onshore.
The study area falls within the western margin of off-
shore depobelt of Niger Delta (Figure 2). The fault pat-
tern is NW- SE and the traps involved in this field are
mainly structural in nature. The study area (KN field) is
within the parasequence set of Agbada formation. Hence,
the local geolog y of the area is similar to that of the Nig-
er Delta. The Niger Delta area is situated in the Gulf of
Guinea be tween long itudes 5˚ and 8˚E and latitude 3˚ and
6˚N [14].
Figure 1 . Map of N iger Delta showing the depobelts [12].
Figure 2 . Location map of t he study are a [15].
3. Materials and Methods
3.1. Data Acquisition
The data used for this research were obtained from an oil
company in Nigeria.
The data provided include 3D seismic data and well
log data. Six wells were drilled on KN-field and are la-
belled KN-1, 2,3,4,5 and 6. Figure 3 shows the base map
of the KN Field.
3.2. Data Processing and Interpretation
3.2.1. Petrophysical Interpretation and Evaluation
Necessary environmental correction that aimed at re-
moving the effect of variable hole size and acquisition
conditions (such as mud weight, salinity, etc.) was per-
formed. Normalization was carried out at scaling the log
response for various wells to a reference well (KN 4), to
provide a homogeneous dataset and attenuate bias from
different vintages and toolset.
A detailed petrophysical evaluation was conducted for
the KN wel ls namel y KN 3, 4, 5 and 6. The GeoGrap hix
software was used to carry out the petrophysical evalua-
tion. The interpretation of the logs in general was per-
formed using a deterministic approach and generated
output curves for shale volume, net to gross, effective
poro sity, effect ive water satur a tion and per meability.
Con tin e ntal p latform
(Upper deltaic plai n )
Platform turbidites
(lower deltaic plain)
Turbidites environment
Afam channel (10.4 My)
Opuama channel (L. Miocene)
Kwa-lboe collapse
(5.47 My)
High gas risk zone
(Deep play )
Paleo-f r on t D e lta
Onshore and Offshore
Main structural limits
Co mp re ss ive fr o nt / belt
Mo b ile s ha le
100 km
Figure 3 . Base map of KN field.
These logs were correlated by picking shale markers to
delineate between reservoir rocks and non reservoir rocks
using GR and resistivity logs. Shale volume was deter-
mined from gamma ray log, Steiber a nd neut ro n - density
was used for shale volume correction.
The porosity was estimated using the available poros-
ity logs (Density and Neutron). The effective porosity
was calculated from the total porosity corrected for shale
The apparent water resistivity was determined from
calculated Rw log of a clean water bearing formation.
Thi s gave a good match with picket plot .
Permeability curve was determined using Timur
model that put also effective porosity into consideration.
Hence, reservoir pay summation using water saturation
cut-off of 0.6; effective porosity of 0.1 - 0.4 and a vol-
ume shale cut-off of 0.3 was used to constrain pay
summation. Tables 1 and 2 proposed by [16] were used
as guides for the clas sificatio n of porosity and permeabil-
ity respectively. Water saturation was estimated using
Indonesia model because of the presence of shale and
was determined using t he formula [17] below:
cl w
R aR
Water Saturation (Sw), True Formation Resistivity Ωm
(Rt), Formation Water Resistivity Ωm (Rw), Shale/Clay
Volume (Vcl), Shale/Clay Resistivity (Rcl), Saturation
Exponent (n), Cementation Exponent (m), Effective
(shale corrected) Porosity
, and Formatio n Factor (F).
Hydrocarbon saturation (Sh), movable oil saturation
(MOS), Residual hydrocarbon saturation (Shr), Hydro-
carbon movability index (HMI) and Water saturation of
flushed zone (Sxo) [18] were evaluated as follow:
Table 1. Q uali tat ive e val u ati on of porosity [16].
Percentage Poros ity (%) Qualitative Evaluatio n
0 - 5 Negligible
5 - 10 Poor
15 - 20 Good
20 - 25 Very good
Over 3 0 Excellent
Table 2. Qualitative evaluation of permeability [16].
Average k-value (MD) Quantitative Description
<10.5 Poor to fair
15 - 50 Moderate
50 - 250 Good
250 - 1000 Very good
>1000 Excell ent
SS= −
xo w
hr xo
SS= −
3.2.2. Seismic Analysis
1) Fault Picking and We ll Tie to Seismic
The identifica tion o f prominent feature s such as major
and minor faults was carried out on the seismic sections
(Figure 4). Well tie to Seismic was done using KN 4
well data and checkshot survey from KN1 to generate
synthetic seismograms, hence, tying the seismic to wells
Figure 5. Hence, the Top of the Sand 9 was expected to
be a seismic +/ zero crossing on SEG normal polarity
Figure 4 . Picking of faults.
Figure 5 . Seismic to well tie using syntheti c seismogram on logs.
section. Synthetic match with seismic was quite good in
KN 4. Picking of Sand 9 Top hydrocarbon sand was
found to be difficult because it fell on a thin line zero
crossing on seismic. Hence mapping was done on the
continuous negative polarity (trough) beneath the zero
crossing a nd bulk shifted.
2) Mapping of Seismic Horizon
The mapping of the horizon was based on synthetic
process. The key seismic reflections which corresponded
to top of main reservoir sa nds were identified on seis mic
data for mapping (Figure 6). The sedimentary section
can be subdivided into three distinct intervals based on
general seismic reflection character, regional studies and
the uniformly blocky, low-value gamma-ray patterns,
some low to high amplitude, parallel and discontinuous
reflection pattern, was observed [11,12,19-21], Upper
Agbada section with thick shale on the sandy sequence
and lower Agbada formation characterized by thick shale,
parallel and high amplitude followed by sand shale in-
tercalation. Although, a chaotic and low amplitude re-
flections interpreted as the Akata formation was also
3) T ime-Dep th Conversi on
The checkshot data was utilized for Sand 9 reservoir in
KN field. This interval represented the deepest hydro-
carbon potential as established from KN wells. The sin-
gle average time-depth funct ion (Figure 7) was used.
4) Seismic Attribute
RMS amplitude was generated for sand 9 in order to
extract information on reservoir characteristics, area ex-
tent and influence of hydrocarbon fluids on seismic re-
3.2.3. Stati c Geol ogi cal Model
In view of the necessity of dynamic simulation process
and to arrive at a final well and production behaviour, it
was necessary to build a static model that represented as
closely as possible the subsurface reality of the Sand 9
that have been encountered by most wells.The static
geological model of Sand 9 for the entire KN Field in
Lower Agbada formation was built by integrating rele-
vant sub-surface data and interpretation presented in the
preceding sections. The 3D seismic structural interpreta-
tion, lithological descriptions and facies interpretation,
porosity, permeability and initial water saturation from
log analyses were used to build the static model. The
PETREL (Version 2009.1) suite was used in building the
static model. The structural model and property model
(net to gross, poro sity, per meability, water saturatio n and
facies) were used for the static modeling of Sand 9 which
are briefly described as follows:
1) Structural Model of Sand 9
The structural model was based on the depth-co n-
verted 3D seismic interpretation. The input data consist
Figure 6 . Top showing sand 9 hor izon.
Figure 7 . T ime -depth funct ion.
of the following: Sand 9 Top Depth surfaces, polygons
and fault surfaces of interpreted faults. Fault modelling
was the first step for b uilding structural models with Pet-
rel workflow tools. The process was used to create struc-
turally and geometrically corrected fault representations
within horizon. Pillar gridding is a way of storing XYZ
locations to describe a surface which was used to gener-
ate a 3-D framework. A 3-D grid divided the space up
into cells within which it assumes materials were essen-
tially the same. The proportional layering was aimed at
capturing equal layer thickness from top of the reservoir
to the base. The reservoir modelled as one major li-
thostratigraphic unit. Thus the structural model was de-
fined by one zone. The faults were modelled mainly from
the input fault surfaces based on interpretation. The areal
dimension of the grid cells was optimized at 50 × 50 m,
considering the reservoir description in Sand 9 prospect.
The grid was oriented parallel to the main northern
boundary fault. The 3D static model contained 260,040
2) Net to Gross Model of Sand 9
The petrophysical evaluation generated a net to gross
log curve from interpretation, which was upscaled. The
distribution of net to gro ss values was done stochastically
usin g Seq uent ial Gau ssia n Si mulat ion ( SG S) wit h t he ne t
to gross value of the reservoir calculated.
3) Porosity Model of Sand 9
The porosity model was based on the porosity logs
generated from petrophysical interpretation of well KN-
3, 4, 5, and 6. The logs were upscaled to the layering
sche me usi n g the fa cie s as a co ntro llin g bi as t hat e nsur ed
that the porosity was appropriate for the facies property
of the cell. The porosity was distributed in the model
using “Sequential Gaussian Simulation” (SGS) that was
conditioned to the facies model and to the wells. A total
porosity cut-off of 7% was applied to the model, consis-
tent with the cut-offs estimated from petrophysical
analysis for the reservoir facies. Multiple realizations
were run on the porosity model conditioned the data to
the respective facies.
4) Permeability Model of Sand 9
The permeability model was based on the permeability
logs generated. This effective in-situ permeability was
distributed in the static model using Sequential Gaussian
Simulation conditioned to the facies of the modelled
zone. A permeability cut-off o f 5 0 mD was applied to the
model, consistent with the log observation. Multiple re-
alizations were run on the permeability model condi-
tioned to the respective facies and respective porosity as
a secondary variable.
5) Wate r Saturation Model of Sand 9
In absence of core data, a deterministic average water
saturation that was derived from petrophysical interpreta-
tion was used in the model. Water saturation value of
26% was used. The model captured the in-put parameters
from the petrophysical analysis.
6) Facies Model of Sand 9
Sequential Indicator Simulation (SIS) technique was
used for this Model. In Sand 9 model, no apparent trends
have been used as the system net-to-gross (NTG) is low
and presumably deposited in marine environment. It was
considered reasonable to use stochastic method for the
modeling. The method allows easy modeling of facies
environments where the facies volume proportions vary
vertically, laterally, or both. Facies associations have
been coded at the wells based on the available log data
(primarily GR) in wells.
The petrop hysical no n-reservoir facies was determined
wit h GR > 7 5 API U nits. T he r ese r voi r facies was further
divided into good and moderate reservoir facies based on
GR distribution. During the facies modeling process, the
facies realizations were conditioned to the wells and
multiple realizations were run in the facies modeling to
capture the inherent heterogeneity if any.
3.2.4. Reservoir Volumetric
Reservoir vo lumetric is the proce ss by which the quantity
of hydrocarbon in a reservoir is estimated. This is very
important because it acts as a guide for field exploration
and development. After a static model of a field was
done, the structural model and the petrophysical model
built were used to calculate the reserves [22] in terms of
stock tank of original oil in place (STOOIP) of the Sand
9 under study were estimated usi ng Equation (3.6).
( )
( )
7758 1
× −××× ×=
A = Area in acres, h = Net Pay Thickness in feet,
= porosity, Sw = Water Saturation, B0 = Formation Vo-
lume Factor, 7758 = Acre-feet conversion for oil.
4. Results and Discussion
4.1. Results
Correlation of six well logs in KN field is displayed in
Figure 8. The summary of the pay is shown in Table 3.
Figure 9 indicates the Top of sand 9 time structure map.
The display of the Top of sand 9 depth structure map is
sho wn i n Figure 10 . The RMS attribute map is presented
in Figure 11. Figure 12 reflects the 3D component of the
structural model of Sand 9. The NTG (Net to Gross)
model is displayed in Figure 13. The porosity model is
presented in Figure 14. The permeability model is dis-
pla ye d i n F igure 15 . The water saturat ion mode l is shown
in Figure 16. Figure 17 shows the facies model. Volu-
metric obtained after modelling is showed in Table 4.
Figure 8 . Six well logs showing structural correlation of KN field.
Figure 9 . Top sho wing time structure map of Sand 9.
Table 3. Pay summary of sand 9.
K Wells Sh Sxo M OS Shr HMI
San d 9Top K N 6 3418.97 3923.2 17.98 17.98 0.21 0.74 0.16 0.27 0.2 0.17
43 Well 06 0.73
0.49 0.22
Sand 9Bas e K N 6 3454.61 3964.45
San d 9Top K N 5 3345.82 3359.54 29.2 25.452 24.2 0.82 0.18 0.32 0.23 0.25
Well 05 0.68
0.49 0.22
Sand 9Bas e K N 5 3375.04 3388.76
San d 9Top K N 4 3376.56 3389.36 37.85 37.83 29.82 0.78 0.17 0.29 0.21 0.19
Well 04 0.71
0.8 0.48 0.2 0.4
Sand 9Bas e K N 4 3414.37 3427.17
San d 9Top K N 3 3398.38 3616.75 65 65 12.27 0.38 0.19 0.29 0.19 0.26
70 Well 03 0.71
0.5 0.23
Sand 9Bas e K N 3 -3430.9 3652.62
Figure 10. Top showing de pth st ructure ma p o f sand 9.
Figure 11. Top showing RMS att r ibute map.
Figure 12. Top showing 3D view components of the struc tural model.
Fault 4
Fault 1
KN 3
KN 6
KN 4
KN 2
KN 5
KN 1
Grid cell si z e 50 x 50
Figure 13. Top showing NTG model.
Figure 14. Top showing porosity model.
Figure 15. Top showing pe rmeabili ty model.
Figure 16. Top showing water saturat ion model .
Figure 17. T o p showing facies mo del.
Table 4. Volumetric obtained aft er modeling.
Zones Bulk V olume Net Volume Pore Volume HCPV Oil STO I IP (in Oil) STOIIP (in Oil) B0 k
*106 m3 STB *106 m3 STB *106 m3 STB *106 m3 STB *106 m3 STB MMbbl
Zone 1 Down th rown 94.23 58. 61 4.58 3.00 15.73 15.73 1.2 6.29
Zone 2 Up t hro wn 0 0 0 0 0 0 0 0
Zone 3 Ramp 17.65 13. 63 1.07 0.73 3.81 3.81 1.2 6.29
4.2. Discussion of Results
4.2.1. Petrophysical Analysis
Figure 8 shows correlation of six well logs reflecting
nine reservoirs with horizon 2 cutting across them. Sand
9 reservoir in KN field was picked between depth of
3385.55 m - 3414.97 m (TVDSS) in Well 1, Well 2
3486.41 m - 3454.55 m (TVDSS), Well 3 3430.9 m -
3398.38 m (TVDSS), Well 4 3414.37 m - 3376.56 m
(TV D SS) , Well 5 3345.82 m - 3375.04 m (TVDSS),
Well 6 3418.97 m - 3454.61 m (TVDSS). All these re-
servoirs were correlated across all the wells to see their
lateral continuity which gives a good description of the
Well 03 is at a total depth of 3500 m (TVDSS) with a
Kelly Bushing (KB) of 45 ft. The base of Benin forma-
tion is at 2290 m. Resistivity of water (Rw) in this well
was determined by considering low gamma response
with low resistivity value. Rw is 0.13 Ωm which was de-
termined at the d epth interva l 34 82 m - 3495 m (TVDSS)
with the true resistivit y (Rt) of 1.5 Ωm. The total depth of
this Well 03 is 3685 m (TVDSS). Well 04 is at 3487 m
(TVDSS) with a Kelly Bushing (KB) of 42 ft. The base
of Benin formation is at 2230 m. Resistivity of water,
(Rw) is 0.08 Ωm which was determined at depth interval
3422 m - 3426 m (TVDSS) with true resistivity (Rt) of
1.0 Ωm. The total depth of Well 05 is 3566 m (TVDSS)
with a Kelly Bushing (KB) of 45ft. The base of benin
formation is at 22 40 m. Resist ivity of water, (Rw) is 0.15
at depth interval 3419 m - 3424 m (TVDSS) with true
resistivity of (Rt) of 1.8 Ωm. Well 06 is at total depth of
4057 m (TVDSS) with a Kelly Bushing (KB) of 34 ft.
The base of Benin formation is at 2280 m. Resistivity of
water, (Rw) is 0.12 Ωm. at depth interval 3470 m - 3460
m with a true r e sistivity (R t) of 1.6 m. Oil/water contact
(OWC) was observed at Well 03 and 01 at depth 3410
m, and 3459 m in Well 02 due to fault compartmentali-
zation, Well 04 and 05 encountered an Oil-do wn-to
(ODT) at 3415 m. Well 06 is more or less a water pool.
Therefore, porosity values 16% - 19% in the four
wells as shown in Table 3 fall within good porosity
(Table 1). These values indicate that the reservoir rocks
in the wells have enough pore space to accommodate
fluids. The permeability values (43 - 76.8 mD) in the
four wells as shown in Table 3 fall within high to very
high permeability.
Generally, water saturation values in the four wells
va ry fro m 2 7% - 32% while Hydrocarbon saturation val-
ues range from 68% - 73%. This shows that the percen-
tage of hydrocarbon that occupies the pore spaces is
more than the percentage of formation water, hence, the
prospective accumulation of hydrocarbon in the reservoir
The values of hydrocarbon movability index (Sw/Sxo)
rangi ng fr o m 0.35 - 0. 4 ar e les s tha n 0.7 in t he fo ur wells
(Table 3) which indicates that hydrocarbon in the four
well s will move. High movable oil saturatio n (0.4 8 - 0.5)
and low residual hydrocarbon (0.2 - 0.23) suggest that
higher percentage of the total hydrocarbon accumulated
in the reservoir will move. Petrophysical results of the
study show that the predominant fluid type in the three
wells is oil. Sand 9 has an “Oil Do wn To” (ODT ) in both
wells 04 and well 05. Three reservoir sands (sand 5, 7
and 9) were delineated , but sand 9 was more promising
because it was characterized by good cap rock facies,
good to very good porosity values (29% - 32%) , l o w wa-
ter saturation (68% - 73%), ac ceptable limit of resisti vity
of formation water, high hydrocarbon saturation (Sh)
(0.68% - 0.73%), good net pay (12 m - 30 m), low resi-
dual hydrocarbon saturation (Shr), high movable hydro-
carbon saturation (MOS) and favourable values of mov-
able hydrocarbon index (Sw/Sxo < 0.7). Sand 9 has the
highest potential due to its high net-pay of about 30 m
and hydrocarbon saturation of about 78%.
4.2.2. Seismic Analysis
The time and depth structure contour maps show system
of different oriented growth faults F1 to F6 (Figures 9
and 10). The fault F1 lies centrally within the mapped
area and extends up to 86% of the entire breath of the
mapped area. A rollover anticline formed as a result of
deformation of the sediments deposited on the down-
thrown block of fault F1. The fault F4 is also extensive
and shows sub-parallel relationship with the fault F1.
This sub-parallel relationship is sustained in all the
structural contour maps. The fault F1 can be interpreted
as the acti ve faul t, while the F 4 is i nactive fault, b ut mus t
have been active in the past and located in offshore di-
rection of F1. The other faults are (2, 3, 5 and 6) both
synthetic and antithetic faults are minor faults. The syn-
thetic faults are syndepo sitional faults while the antithetic
faults were formed as a result of tectonics after deposi-
tional has stopped. It can be deduced fro m this study that
the wells were located to target the rollover anticline
formed on the downthrown side of the fault F1 which
indicates structural closure in these areas.
4.2.3. Static Modeling
1) Structural Model of Sand 9
Figure 12 indicates the system of different oriented
growth faults F1 to F6. Faults 1 and Fault 4 are the major
growth faults, dipping towards south-west and are quite
extensive. The other faults are (2, 3, 5 and 6) which are
indicative of synthetic and antithetic faults are catego-
rised as minor faults. This model further buttr esses the
information gathered from t he depth struct ure map .
2) Net to Gross map in Sand 9
Figure 13 reveals good net to gross which falls be-
tween 0.8 and 1 within the well area (3, 4, 5 and 6) of the
KN field while the region farther away from the well
location is indicati ve of low net to gross which oscillate s
between 0 and 0.1.
3) Porosity Map in Sand 9
A 3D perspective view of the porosity model is shown
in Figure 14. The map shows the prominence of good
porosity distribution (0.15 - 0.20) within well area (3, 4,
5 and 6) of the KN field. This indicates the pore spaces
have enough space to accommodate fluid while the re-
gion farther away from the well location in the northern
part and some parts in the south-west direction indicate
porosity range from 0% - 2.5% which indicate poor po-
rosity (Tabl e 1).
4) Permeability Map in Sand 9
Figure 15 shows a 3D perspective view of the per-
meability model. The map underscores good permeabili-
ty val ues whic h range fr om 50 mD to 100 mD (Ta ble 2)
withi n the well area (3, 4, 5 and 6) of KN field. The val-
ues are reflective of good interconnectivity of pore spac-
es of the sand within the well area and their ability to
trans mit flui ds. In co ntrar y, th e region fart her awa y fro m
the well location in the northern part and some parts in
the south-we st direction indicate poor to fair permeability
which ranges fr om 1 mD to 5 mD.
5) Water Saturation Map in Sand 9
Figure 16 shows a 3D perspective view of the water
saturation. The map reveals that water saturation distri-
bution withi n the well area (3 , 4 , 5 a nd 6) of the KN field
varies from 0.3 to 0.4. This is indicative of more hydro-
carbon zone region. The region farther away from the
well location has water saturation more than 0.75. The
resul t shows ab undant of wat er t han hydroca rbo n.
6) Facies Map in Sand 9
A 3D perspective view of the facies model is shown in
Figure 17. T he map s hows fa c ie s wit hi n t he well area (3,
4, 5 and 6) of KN field indicate moderate to good reser-
voir (sand) while the regions farther away from the well
location along the north and south west directions reveal
shale (poor reservoir). The Sand 9 reservoir shows clear
abundance of shales deposited in a trangressive marine
environment with minor influence of tides in marine
condition. Poor facies are more than the good facies in
the reservoir rocks which is an indication of shaliness in
Sand 9. The regional depositional direction assumed is
NW-SE directio n.
4.2.4. Reservoir Volumetric
Table 4 reveals volumetric after modeling. This shows
that the downthrown segment has bulk volume, pore vo-
lume, net volume, HCPV Oil and STOIIP values of 9.40
× 107 m3 STB, 5.80 × 107 m3 STB, 0.45 × 107 m3 STB,
0.3 × 107 m3 STB and 1.50 × 107 m3 STB while the
Ramp segment has bulk volume of 1.80 × 107 m3 STB,
net volume of 1.30 × 107 m3 STB, pore volume of 0.11 ×
107 m3 STB, HCPV Oil of 0.73 × 106 m3 STB and
STOIIP of 3.81 × 106 m3 STB. The sand 9 reservoir
shows that the downthrown and ramp segment indicate
hydrocarbon of commercial value thus, the Sand 9 static
model could be as input for simulation and performance.
5. Conclusions
This research work shows the versatility of integrating
3D seismic reflection and well log data for reservoir
modeling. The results of the comprehensive petrophysi-
cal analysis of the six wells show one dominant reservoir
across the well 01 and 06 in the entire field at different
depth intervals. This sand 9 reservoir is very promising
because of its good to very good porosity values, low
water saturation, high hydrocarbon saturation (Sh), high
movable oil saturation (MOS), low residual hydrocarbon
saturation (Shr), low values of hydrocarbon movability
index ( Sw/Sxo < 0.7), good permeability and moderate net
to gross. Also, the mapped sand 9 horizon indicates hy-
drocarbon accumulation in economic quantity. The ac-
cumulation and trapping of hydrocarbon in this field is as
a result of the rollover structures due to faulting. The
trapping mechanism is a fault a ssisted closure.
The discrete properties gave the knowledge of the fa-
cies properties in the field while the continuous proper-
ties gave the petro ph ysical prope rties of the field in ter ms
of porosity, permeability, net to gross and water satura-
tion. The facies analysis indicates that both good and
moderate sand quality are found in the sand 9 reservoir
which support the properties from petrophysics in terms
of porosity, net to gross and permeability. The volume-
tric calculation indicate s that the do wnthrown segment of
the reservoir has a STOIIP of 15.73 MMbbl of oil and
the Ramp segment has a STOIIP of 3.81 MMbbl. This
analysis will serve as a control of the reservoir during
The 3-D Static Modeling of the KN field has provided
a better understanding of the spatial distribution of the
discrete and c ontinuous pro perties in the field. T he study
has also created a geological model for KN field that can
be updated as new data acquired for field development.
The model could be transferred to reservoir engineer for
proper characterization during simulation. However,
more horizontal wells should be drilled to enhance opti-
mization of the reservoir.
Ackno wledgements
The authors wish to thank the Department of Petroleum
Resources for authorizing one of the oil companies to
release the data for this research.
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