Journal
of Science
Research
Facies model building of integrated multiscale data in Dn-Field,
Onshore, Niger Delta, Nigeria
Nton, M. E. and Arigbe, O. D.
Department of Geology, University of Ibadan, Nigeria
Correponding author: ntonme@yahoo.com
Abstract
This study employs 3D Post-Stack Time-Migrated seismic data from the DN-Field, within the Coastal Swamp depobelt of
the Niger Delta in predicting lithofacies and fluvial facies of OVK-1 sand bodies in the Agbada Formation, as a tool to
identify new drillable prospects. A lithofacies model for OVK-1 reservoir sand body was generated after upscaling using
Most Of, as the averaging method. Calibrated by fluvio-facies at the well locations, channel sands were identified in OVK-
1 reservoir interval using Stochastic Sequential Indication Simulation (SSIS) algorithm. Based on lithofacies, fluvial facies
and biofacies analyses, a terrigenous and shallow fluvio-deltaic fill within a lowstand system tract was evident. Petrophysical
properties including porosity, volume of shale and effective porosity were upscaled, guided by facies model and then
Stochastic Gaussian Simulation (SGS) algorithm was used to produce the model. Porosity model predicted sand layers
having maximum porosity of 27.5% which implied very good reservoir potential. However, the volume of shale model with
values from 0.45 to 0.50 incorporates silt and clay and indicates marginal reservoir potential. The study identifies four
potential reservoir intervals with thickness ranging from 9.1 to 38.5 m. The effective porosity in OVK-1 ranges from 0.10 to
0.30 and identified fluvial facies such as floodplain, channel sand, levee and crevasse splay sand. Facies model show a
good sand distribution with minor shale localized in the western part of the Field. The central part of the model has good
reservoir qualities, evident by low volume of shale values and high porosities. This study helps to identify a potential
unexplored drillable prospect on OVK-1 sand body south-west of DN-2 well. Successful drilling of the identified prospect
could increase the reserve of the Field.
Keywords: Facies modeling; stochastic sequential indicator simulation; sequential gaussian simulation; Niger Delta;
Nigeria.
Introduction known accumulation of recoverable hydrocarbons, with
The Niger Delta is an active sedimentary basin [1] reserves exceeding 34 billion barrels of oil and 93 trillion
which is situated in the Gulf of Guinea (Figure 1a). It cubic feet of natural gas [7]. There is renewed
lies between Latitudes 3o and 6o N and Longitudes 5o exploration interest currently in the deepwater
and 80 E, and extends throughout the Niger Delta depositional systems, as about 50% of global oil
province as defined by Klett et al [2]. From the Eocene production is currently from shallow marine, paralic
to the present, the delta has prograded south- and fluvial strata [8]. This necessitated the application
westwards, forming depobelts that represent the most of improved approaches for facies modelling in order
active portion of the delta at each stage of its to properly integrate multi-scale data in the exploration
development [3]. These depobelts form one of the and field development of the vast hydrocarbon
largest regressive deltas in the world, with an area of resources locked in these areas of the Niger Delta
300,000 km2 [4], sediment volume of 500,000 km3 [5], basin.
and thickness of over 10 km in the basin’s depocentre Facies modelling is a critical step in the life cycle of
[6]. a reservoir characterization process. All petro-physical
The Niger Delta is the most prolific sedimentary modelling is based on facies; geometric distributions
basin in sub-Saharan Africa, containing the 12th largest are determined by geologic knowledge of facies
Volume 14, 2015, pp. 107-116.
Journal of Science Research, 2015
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108    Journal of Science Research Vol. 14
S t u d y
a r e a
Study area
Figure 1a. Map of Nigeria showing the location of Niger Delta and associated tectonic features. Inset is the Map of Africa
showing southern Nigeria (Corredor et al 2005).
deposition and the flow units controlling the production found the reservoir lithology to be grossly
of a reservoir are generated directly from facies, or heterogeneous. Afuye et al [13] developed 3D
representation of the facies distributions [9]. As rightly depositional and lithofacies models using Truncated
remarked, a common challenge of facies modelling is Gaussian with Trends (TGT) and Truncated Gaussian
the integration of multiple scale data to create a reliable Simulation (TGS) algorithms. With the available data
facies model [9]. set for this work, Sequential Indicator Simulation
The sequential indicatior simulation (SIS) technique algorithm was found appropriate and ideal for producing
is a statistical tool that can be used to simulate a large the reservoir facies model with the limited well control.
number of equiprobable realizations of a particular This study attempts to predict reservoir facies of
property or attribute. It is a cell- (pixel) based modelling OVK-1 sand bodies using stochastic sequential
algorithm that uses the upscaled well observation as indicator simulation, facies architecture and petro-
the basis to segregate the facies types to be modelled physical properties to identify potential unexplored
and also honour semivariogram (commonly referred drillable prospects. This no doubt if drilled, will help
to as variogram) and trends to constrain the distribution boast additional reserves in the Niger Delta Basic.
and connection of each facies as well as the histogram.
Journel [10] states that the differences between the Study area and geology
realizations themselves should provide a measure of The study area, DN Field, lies within the coastal swamp
spatial uncertainty about the input. This method is depobelt (Figure 1a) of Niger Delta and it is operated
particularly useful as it produces geologically by Shell Petroleum Development Company of Nigeria
reasonable results in data-challenged areas with few Limited. The in-lines and cross-lines are in the ranges
well controls [11]. of 11,228 to 12,028 and 2,673 to 3,373 respectively
Ataei [12] applied the principle in the modelling of a with a spacing of 100 m between lines (Figure 1b).
turbidite reservoir in the Gulf of Mexico, where he The onshore portion of the Niger Delta Province is
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Nton and Arigbe: Multiscale data in Dn-Field onshore 109
M a p
C o u n t r y S c a l e1 . 6 2 5 0 0
B l o c k C o n t o u r  i n c
S y m b o l  l e g e n d
L i c e n s e U s e r  n a m e
F D E R O L T + U n d e f i n e d
M o d e l  n a m e D a t e
0 4 / 1 8 / 2 0 1 3
H o r i z o n  n a m e S i g n a t u r e
Figure 1b. Base map of DN Field showing the positions of the three Wells within the Field available for this study.
delineated by the geology of southern Nigeria and oldest to youngest; the marine shales of the Akata
south-western Cameroon. The northern boundary is Formation, middle paralic Agbada Formation and the
the Benin Flank, an east-north-east trending hinge line topmost Benin Formation [1, 14].
south of the West Africa basement massif. The north-
eastern boundary is defined by outcrops of the Akata Formation
Cretaceous on the Abakaliki High and further east- The Akata Formation, the oldest formation in the Niger
south-east by the Calabar Flank, a hinge line bordering Delta, is of Paleocene age to the present. It comprised
the adjacent Precambrian Basement Complex [1]. The pro-delta marine shales with local sandy and silty beds
offshore boundary of the Niger Delta Province is which have been transported to deep water areas. The
defined by the Cameroon volcanic line to the east, the sediments are characterized by low energy conditions
eastern boundary of the Dahomey Basin to the west, and oxygen deficiency [15] laid down as turbidites and
and the 2 km sediment thickness contour or the 4,000 continental slope channel fills. It is estimated that the
m bathymetric contour in areas where sediment formation is up to 7,000 meters thick [3]. The formation
thickness is greater than 2 km to the south and south- underlies the entire delta, and is typically overpressured.
west. The province covers an area of approximately Turbidity currents likely deposited deep sea fan sands
300,000 km2 and includes the geologic extent of the within the upper Akata Formation during development
Tertiary Niger Delta (Akata-Agbada) Petroleum of the delta [16]. This formation constitutes the
System. effective source rocks in the Niger Delta.
Stratigraphy Agbada Formation
The sedimentary sequences of the Niger Delta are This formation overlies the Akata Formation in the Niger
made up of three stratigraphic units which are from Delta and is made up of paralic sands and shales. The
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110    Journal of Science Research Vol. 14
sands constitute themain petroleum-bearing unit in the some loading steps for the data already in the industrial
Niger Delta while the shales provide lateral and vertical format acceptable to the software. The header
seals [17]. Deposition of the Agbada Formation, began information and mapping coordinates were carefully
in the Eocene and continues into the Recent. The inputted during the loading exercise. To effectively
formation is over 3,700 meters thick and represents cover the study area, the seismic data was interpreted
the actual deltaic portion of the sequence. The clastics at a scroll increment of 10 lines on both in-lines and
accumulated in delta-front, delta-topset, and fluvio- cross-lines. The well data was in the ASCII format
deltaic environments. In the lower Agbada Formation, and was also imported into the Petrel 2009.1™
shale and sandstone beds were deposited in equal software. The methodology involved making additional
proportions, however, the upper portion is mostly sand well logs like volume of shale, seismic attribute
with only minor shale interbeds. generation, facies modeling, petrophysical modeling,
determination of the environment of deposition using
Benin Formation well logs and biofacies.
The Benin Formation is the youngest stratigraphic The work flow for the different aspects of the study
sequence in the Niger Delta. It is about 2,000 m thick is shown in Figure 2.
, and consists mainly of fresh-water fluvial sands and D a t a  S e t
gravels which are occasionally interspersed with shale
beds towards the base of the unit. The sands are 3 D  S e i s m i c W e l l  l o g s C h e c k s h o tV o l u m e D a t a
generally fine to coarse-grained and very poorly sorted.
F a u l t V o l u m e E s t i m a t e d F a c i e s
Occasional streaks of lignite and thin scattered grayish I n t e r p r e t a t i o n A t t r i b u t e s C o r r e l a t i o n L o g s I n t e r p r e t a t i o n
brown shale beds are intercalated with the sands and
gravels. The grains are subangular to well rounded S y n t h e t i cS e i s m o g r a m
and varies in colour from clear white to yellowish brown
quartz with subordinate hematite and feldspar grains
[14]. It ranges in age from Oligocene to Recent. H o r i z o nI n t e r p r e t a t i o n
Structures T i m e  a n d
The Niger Delta is subtly disturbed at the surface but D e p t h  M a p s
the subsurface is affected by large scale
synsedimentary features such as growth faults, rollover G e o l o g i c a lG r i d s
anticlines and diapirs [3,15]. The structural style, both
on regional and on the field scale, can be explained on S c a l e  u p
the basis of influence of the ratio of sedimentation to W e l l  l o g s
subsidence rates. The different types of structures are
namely, simple non-faulted anticline rollover anticline D a t aA n a l y s i s
with multiple growth faults, or anticline faults and
complicated collapse crest structures [18]. Other F a c i e s
structures are sub-parallel growth fault (k-block M o d e l l i n g
structures) and structural closures along the back of
growth faults. P e t r o p h y s i c a lM o d e l l i n g
Materials and methods
Figure 2. Workflow of study.
Dataset
The dataset used for this study includes 3-D seismic, Deductions from well logs
composite logs of three wells, biofacies data of the Well log data (GR, Vshale, Porosity, FDC well log suite)
three wells and checkshot data containing velocity were used to train a seismic classification. This is based
information. The three wells used for this study are on the fact that these logs provide first hand information
namely; DN-1, DN-2 and DN-3 with respective total about the properties of a Formation before using seismic
depths; 7,700 ft (2,333.3m), 7,500 ft (2,272.7 m) and attributes as a follow-up. Seismic volume attribute
10,000 ft (3,030.3 m). The dataset were obtained from cubes (Figure 3) notably; acoustic impedance, iso-
the Shell Petroleum Development Company of Nigeria. frequency and envelope, capable of recognizing the
The 3-D seismic data, which was in ZGY bricked properties of interest such as porosity, volume of shale
format was imported into the Petrel 2009.1™, following and effective porosity respectively were generated.
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Nton and Arigbe: Multiscale data in Dn-Field onshore 111
distribution were used to create local variations even
away from input data. Fluvial facies interpretations
from well logs were upscaled and stochastic object
modeling algorithm was used to produce a fluvial facies
model.
Palaeodepositional environment
Palaeodepositional environment was determined based
on gamma ray log shapes as reported by Morris and
Biggs [21] and biofacies analysis result. According to
Emery and Myers [22], clean-up, dirtying-up and
boxcar trends can be recognised when examining well
log curves. Selley [23] opined that the environments
of shallowing-upward and coarsening successions is
divided into three categories namely; regressive barrier
bars, prograding marine shelf fans and prograding delta
or crevasse splays. Boxcar trend could indicate a slope
channel and inner fan channel environments [24].
According to Emery and Myers [22], the greater
range of thickness indicates turbidites sands and lesser
thicknesses indicate inner fan channel environments.
In a non-marine setting, dirtying-upward is predominant
within meandering or tidal channel deposits with an
upward decrease in fluid velocity within a channel [22].
The irregular shape has no character, representing
aggradation of shales or silts and in the analysis
classifies the log facies as belonging to a flood plain
environment.
Biofacies analysis was based on a checklist
constructed by occurrence, abundance and diversity
Figure 3. Seismic volume attributes showing acoustic of planktonic and benthonic foraminifera, and pollens
impedance, iso-frequency and envelope respectively. [25]. Maximum abundance and diversity values
generally reflect transgressive conditions and minimum
In this study, a Principal Component Analysis (PCA) values reflect regressive conditions.
was first run on all the properties to be modeled and
the correlation coefficient ascertained. Principal Results and discussions
Component Analysis (PCA) is a technique for Based on well logs and seismic interpretation, a number
simplifying any dataset by reducing the multidimensional of models and cross plots were generated. Four
dataset to lower dimensions for analysis. As reported reservoir sands were identified namely; OVK-1,
by Jungmann et al [19], principal component analysis OVK2, OVK-3 and OVK-4 with intervals from 4,645
and stepwise discriminant analysis are two major to 4,700 ft [ 1407.6 to 1,424.2 m]; 4,773 to 4,864 ft
methods used for feature extraction; they help to [1,446.4 to 1,473.9 m], 5,748 to 5,778 ft [1,741.8 to
emphasize variation and bring out strong patterns in 1,750.9m] and 5,889 to 6,016 ft [1,784.5 to 1,823.0 m]
the dataset which can improve the accuracy and respectively. In this study, OVK-1 reservoir sand being
stability of classifiers by removing unwanted, non- the reservoir of interest has an interval of 4,529-4,584
distinctive and interrelated features. The already ft [1,372.4 to 1,389.1 m] in DN-1 well and 4,562-4,617
generated lithofacies model was used to constrain the ft [1,382.4 to 1,399.1 m] in DN-2 well and 4,645-4,700
petrophysical models generated. The properties to be ft [1,407.6 to 1,424.2 m] in DN-3 well (all depths are
modeled such as volume of shale and effective porosity in SSTVD) (Figure 4).
were first upscaled and Stochastic Gaussian Simulation
(SGS) algorithm was utilized to produce the model. It Lithofacies’ model
should be noted that Sequential Gaussian Simulation Stochastic sequential indicator simulation algorithm
(stochastic) uses well data, input distributions, approach was used to generate a lithofacies model for
variograms and trends [20]. The variograms and OVK-1 reservoir sand body. This was achieved by
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112    Journal of Science Research Vol. 14
Figure 4. Well log correlation and biofacies information of DN-Field Wells showing the potential reservoir intervals, the
seals and continuity across the wells.
assigning discrete values to each point to show sand deposits (Figure 6). Clean reservoir sands and marginal
and shale. Sand was assigned a value of ‘0’ and colour shaly sands are superimposed on top of the background
coded as yellow, fine sand was assigned a value of ‘1’ facies. The flanks of the reservoir area have more
and colour coded as orange while shale was assigned
a value ‘2’ and given a grey colour (Figure 5); this is
as opposed to property modeling, that is continuous,
which was able to show the gradual lateral change of
petrophysical properties towards the flanks of the
reservoir unit. In the lithofacies model, the central part
of the OVK-I reservoir sand body shows cleaner
sands (Figure 5) with minor shale development in the
western part of the Field. In the fluvial facies model,
majority of the channels are concentrated there with
attendance highest values of porosity and hydrocarbon
saturation recorded. Modelled channels show geometry
and N-S orientation within the Agbada Formation
across the field as shown by the compass direction
(Figure 6). It could also be seen that all the wells fall Figure 5. Facies model of OVK-1 reservoir showing   good
within the channel sand area, having the highest porosity. sand distribution and  minor shale in western part with
The fluvial facies model indicates areas covered by positions of existing wells. Enclosed loop shows the
channel sands, back ground flood plains, and levee identified drillable prospect.
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Nton and Arigbe: Multiscale data in Dn-Field onshore 113
Figure 6. Fluvial facies model of OVK-1 reservoir with Figure 8. Effective porosity model of OVK-1 reservoir with
positions of existing wells showing distribution of ranging between 0.2 and 0.4.
background flood plains, channel sand , levee and crevasse
splay sands.
Figure 7. Volume of shale model of OVK-1 reservoir; areas Figure 9. Porosity model of OVK-1 reservoir following
with blue colour have the best reservoir qualities, followed the distribution of volume of shale;   central portion of the
by areas with yellow/green while areas in red are highest model has the highest porosity values.
volume of shale.
flood plain materials such as shales and silts, hence upscaled using ‘Arithmetic Mean’ as the averaging
the very high volume of shale (Figure 7) and low method as they are continous variables as opposed to
effective porosity (Figure 8). facies which is discrete. The grid was calibrated into
fractions which define the 3D model into various
Petrophysical properties depositional environments; the part which captures
Petrophysical properties which include porosity, volume values of 0.15-0.23, signifies potential reservoir rocks
of shale and effective porosity were modeled. The while areas with high volume of shale are poor
distribution of petrophysical properties gives clues to reservoirs. Bamidele and Ehinola [27], reported the
the petroleum potential of the DN-Field. The areas volume of shale as ranging from 0 to 0.65 for offshore
with high porosity values having shades of yellow and Niger Delta. The effective porosity model gives the
red, ranges from 19 to 27.5% and are potential areas degree of interconnectivity of the reservoir. In this study,
for hydrocarbon prospecting (Figure 9). The areas with areas with blue colour which capture 0.20-0.40, show
low values of effective porosity allow little or no flow high effective porosity while those with purple colour
of hydrocarbon (Figure 8). As reported by Ajibola and in the model indicate regions in the DN-Field with low
Brian [26], the porosity values of the Agbada Reservoir effective porosity values.
Sands range from 10 to 30 %, with formation thickness
in the order of between 9,600 to 14,000 feet. Lithological identification
The volume of shale (Figure 7) represents the The different cross plots show overlay of points with
distribution of properties from the upscaled version of common lithology. As reported by Schlumberger [20],
the well logs. The volume of shale as well as other responses of the logging tools used in any particular
petrophysical properties modelled in this work was interval, will plot as a point on a crossplot. These
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114    Journal of Science Research Vol. 14
crossplots give a quick view of the lithology in qualitative 1,749 m), and 6,504-6,456 ft ( 1,971-1,956 m) (DN-1);
and quantitative ways. The gamma ray/density and 5,516-5,550 ft (1,672-1,682 M) (DN-2) and 5,444-5,461
gamma ray/neutron crossplots for DN-3 well (Figure ft (1,650-1,655 m)(DN-3) (Figure 3). Blocky trend,
10) show that the reservoir sand (shades of red) and characterised by sharp upper and lower boundaries
shaly facies (shades of purple) plot in same zone, occur at intervals; 6,071-6,143 ft (DN-1), 7,133-7,200
implying the reservoir facies to be dominantly sand ft (DN-2), 5,894-5,952 and 6,200-6,234 ft (DN-3). It is
with some silt. observed that the sands here are not as thick as 25 m,
hence they are in inner fan channel environment as
Palaeodepositional environment reported by [22]. The irregular trend in DN-1 well,
The log shapes of OVK-1 reservoir at interval between showing from a depth of 6,200-6,510 ft and 6,250-6,525
4,678 (1,417 m) and 4,700 ft (1,424 m) in DN well 3 ft in DN-2 well, classifies the log facies as flood plain
(Figure 3) can be inferred to indicate a crevasse splay. [22]. The environment is characterised by a blanket of
This 28 ft (6.7 m) thick reservoir unit according to clays and silts, deposited from suspension, with high
Chow et al[28], is comparatively thin to be a prograding lateral continuity and low lithologic variation related to
delta. Crevasse splay deposits also occur at intervals a gradual upward change in the clay mineral content
5,368-5,414 ft (1,627-1,641 m), 5,741-5,771 ft (1,740- or an upward thinning of sand beds in a thinly
Figure 10. Lithological identification from GR/FDC and GR/CNL crossplots of DN-3 well areas with red and blue  signifying
coarse and fine sand facies respectively while  grey   represents the shale facies.
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Nton and Arigbe: Multiscale data in Dn-Field onshore 115
interbedded sand-shale unit, both of which imply a (SSTVD), having high foraminifera diversity, all
decrease in depositional energy. correlate with the shales and silts of both the flood
From the biofacies data of DN-3 well (Table 1), plain and levee which are low energy depositional
depths 4,700-4,782 ft ( 1,424-1,449 m), 4,816-4,834 ft environments. There are very little benthic foraminifera
(1,459-1,465 m), 4,864-4,993 ft (1,474-1,513 m), 5,027- in the study-area but an abundance of planktonic and
5,056 ft (1,523-1,532 m) , 5093-5,230 ft (1,543-1,585 shallow marine forams, validating the environment of
m), 5,269-5,382 ft (1,597-1,631 m), 5,467-5,623 ft deposition as fluvio-marine.
(1,657-1,703 m), 9,200-9,818 ft (2,788-2,975 m)
Table 1. Biofacies interpretation for DN-3 well showing the environment of deposition and population  of both benthic and
planktonic foraminifera.
S/N Depth Type Environ- Foram Foram Plankton Plankton
ment diversity population diversity population
1 5000 3 B 0 0 0 0
2 5030 3 B 0 0 0 0
3 5060 3 B 0 0 0 0
4 5090 3 B 0 0 0 0
5 5120 3 SH.IN 2 5 0 0
6 5150 3 SH.IN 1 2 0 0
7 5180 3 SH.IN 1 8 0 0
8 5210 3 B 0 0 0 0
9 5240 3 B 1 1 0 0
10 5270 3 B 0 0 0 0
11 5300 3 SH.IN 2 7 0 0
12 5330 3 IN-MN 8 14 2 2
13 5360 3 IN 5 12 0 0
14 5390 3 IN 3 11 0 0
15 5420 3 IN 1 4 0 0
16 5450 3 SH.IN 1 4 0 0
17 5480 3 MN 10 14 2 2
18 5510 3 IN-MN 8 21 2 3
19 5540 3 SH.IN 2 2 0 0
20 5570 3 IN 5 8 1 1
Conclusions new drillable prospect, located on clean sand, with high
Facies model building of integrated data in Dn-Field, porosity and good effective porosity has been
onshore Niger Delta was undertaken to predict identified. The identified drillable prospect is southwest
lithofacies and fluvial facies of OVK-1 sandbodies in of DN-2 well, coinciding with 3,500 m contour line on
the Agbada Formation, as a tool in identifying new the structure map. However to better ascertain
drillable prospects. A stochastic sequential indicator lithofacies both horizontally and vertically, it is
simulation method which uses a nonlinear function with imperative that core data be provided. A chronology
better connectivity, higher repeatability and shorter turn- of depositional episodes can be established from careful
around time, was used to generate a lithofacies model analysis of cross-cutting relationships observed on core
for OVK-1 reservoir sand body in the Niger Delta. pictures and seismic time slices at different times.
Calibrated by fluvio-facies at the well locations, channel
sands were identified in OVK-1 reservoir sands with Acknowledgments
the channels concentrated in the central part, where The authors are grateful to the Shell Petroleum Development
there are more sands in the lithofacies model. Based Company Limited, Nigeria, for provision of dataset for this
on lithofacies, fluvial facies and biofacies analysis, a study. We are grateful to the Department of Geology,
terrigenous and shallow fluvio-deltaic fill, within a University of Ibadan, for accessibility to the workstation
lowstand system tract is evident. for the data interpretation.
Porosity model predicts sand layers to have maximum References
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Journal of Science Research
Volume 14,  2015, ISSN 1119  7333
Citation: Nton, M. E.  and Arigbe, O. D.
Facies model building of integrated multiscale data in Textflow  Limited
Dn-Field, Onshore, Niger Delta, Nigeria. Ibadan, Nigeria
UNIVERSITY OF IBADAN LIBRARY