INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4371
Reservoir Characterization through the Application of Petrophysical
Evaluation of Well Logs of Animaux Field, Niger Delta Basin,
Nigeria
Essiet, Aniekan M
1
, Nnabo, Paulinus N
2
, Ani, Chidiebere C.
3
and Oboh, E.Goodluck
4
1, 2, 3
Department of Geology, Ebonyi State University, Abakaliki-Nigeria
4
Multisub Energy Limited, Lagos-Nigeria
DOI: https://doi.org/10.51244/IJRSI.2025.120800396
Received: 16 September 2025; Accepted: 24 September 2025; Published: 18 October 2025
ABSTRACT
Reservoir characterization through the application of petrophysical evaluation of well logs was carried out over
Animaux Field in the Niger Delta Basin of Nigeria. A suite of well logs from three wells (Ani-1, Ani-2, and
Ani-3) were evaluated and used for reservoir characterization. Five hydrocarbon reservoirs containing oil, in
three levels and oil and gas in two levels were interpreted from the three wells. Petrophysical properties (net-
to-gross, thicknesses, water saturation and porosity) were estimated over five reservoirs to characterize the
quality as well determine fluid typing of the reservoirs. Well correlation was carried out to find out the
connections between the three wells to determine connectivity and extent. The Petrophysical analysis produced
an average porosity value of 28% for the Ani-1 well for the various zones and water saturation of 31%, 21%
and 20% for the E, G, and H oil zones respectively. Reservoirs E, G, H, and L have an average net pay
thickness of 3.05m, 7.62m, 3.05m and 6.59m for the oil zones respectively, while the net pay thickness for
reservoirs G, H and H_1 gas zones are 11.28m, 8.99m and 9.91m respectively. The reservoir parameters
obtained show that the reservoirs are good and of high quality.
Keywords: Reservoir, Characterization, Petrophysical properties, well log, Animaux
INTRODUCTION
The role of Oil and gas field evaluation in global energy sustainability and economic prosperity cannot be
overemphasized. The economic worth of an oil and gas company depends on its hydrocarbon reserves which
are used by shareholders and investors as the present and future strength of the company (Emujakporue, 2016).
Therefore, there is need to be sure of the prospects of an oil field before embarking on the exploration of the
oil field as it could be counterproductive and end up with a dry hole especially when all geological conditions
such as source rock, reservoir rock, traps, seal and migration required for the field to be productive are not
present.
Reservoir characterization refers to the process of unfolding all the features of the hydrocarbon-bearing
reservoir, which necessitates the utilization of the most accurate measurements since considering all the
characteristics of the reservoir are pertinent to its ability to store and facilitate fluid flow (Ganguli and Dimri,
2023). Log analysis is used to describe the porosity, lithology, and geometry of the pores, in addition to
permeability and is often used to provide estimated interpretation on reservoir level of oil (Asquith and
Krygowski, 2004).
The aim of this study is reservoir characterization through the application of petrophysical evaluation of well
logs data of Animaux Field, Niger Delta Basin, Nigeria with the objectives of determining volume of shale,
porosity, water saturation, hydrocarbon saturation, gross rock volume, net-to-gross ratio, net rock volume of
hydrocarbon bearing reservoirs.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4372
Location and Geology setting of the Study Area
Animaux Field is located in the coastal fringe of the Niger Delta Basin and in swamp terrain (Figure 1). The
Niger Delta swamp is a tropical rain forest with marshy land, brackish water and green vegetation in southern
Nigeria. The field can be accessed through rivers, creeks and creeklets.
Figure 1: Map of the Niger Delta showing the location of the Study Area -Animaux Field (modified after
Amangabara and Obenade, 2015)
Evolution of the Niger Delta is closely linked to the geodynamics related to the separation of the African and
South American continents and the tectonics of the formation of the Benue Trough (Najime et al., 2017).
Rifting in this basin started in the Late Jurassic and ended in the Mid Cretaceous (Lehner and De Ruiter,
1977). The stratigraphy of Niger Delta has been noted to be tripartite lithostratigraphic succession (Short and
Stauble, 1967). These tripartite lithostratigraphic units are recognized in deep well sections as vertical
subdivision of formation types. Figure 2 depicts the Stratigraphic column showing the three formations of the
Niger Delta.
Figure 2: Stratigraphic-column-showing-the-three-formations-of-the-Niger-Delta (Lawrence et al., 2002;
Corridor et al., 2005).
The three Formations are: the transgressive marine Akata shale, the petroliferous parallic Agbada Formation
and the continental Benin sands. Shales are major cap rocks which act as seals while sands and/or sandstones
are the reservoirs that entrap the hydrocarbon in the Niger Delta (Adagunodo and Akinlabi, 2024). The Agbada
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4373
Formation is the major oil and natural gas-bearing facies in the basin. This sequence is over 4,000 m thick, but
thicker at the central part showing that the depocentre is located in the central Niger delta (Evamy et al. 1978).
Data Availability and Quality
The available data for petrophysical evaluation includes a suite of well logs from Ani-1, Ani-2 and Ani-3,
deviation survey data, formation well tops and checkshot data fromAni-1. The suite of logs contains gamma
rays, spontaneous potential, density, neutron, and resistivity logs. There is no core data to aid formation
evaluation. Table 1 shows the availability of petrophysical data or otherwise. Green indicates availability while
red indicates the non-availability for a particular well log.
Table 1: Animaux Field well data availability
LITHOLOGY
RESISTIVITY
POROSITY
WELL
NAME
SP
CAL
GR
RES
RHOB
NPHI
DT
Well log Header
Information
DEV
Ani-1
Ani-2
Ani-3
Not Available
Available
MATERIALS AND METHODS
Well Log Correlation
Well Log correlation was carried out across the wells using the available log suite from each of the wells. The
correlation of wells was used to define trends of petrophysial data across the field and to determine
connectivity and extent. Figure 3 shows the Well correlation panel for Ani-1, 2 and 3 showing the reservoirs.
The five reservoirs : E, G, H, K, and L were identified as hydrocarbon bearing (see table 2 and 3). The
reservoirs are made up of mainly sandstones, shaly sand and sandstone with shale intercalation based on the
log signature.
Figure 3: Well correlation panel for Ani-1, 2 and 3 showing the reservoirs
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4374
Petrophysical Evaluation Methodology
Using the available data in Table 1 above, the formation evaluation methodology in this study consisted of:
(1) Data Preparation and loading (LAS format) into Techlog.
(2) Logs QA/QC.
(3) Logs normalization.
(4) Fluid typing and Contacts assumptions.
(5) Vshale Evaluation/Net to Gross (NTG).
(6) Porosity Evaluation.
(7) Sw Evaluation.
(8) Pay Cut-off Analysis.
The well logs in LAS file format were loaded into Techlog software. The LAS (Lidar LASer) file format is a
binary file format specifically designed for storing lidar point cloud data. It was developed and is maintained
by the American Society for Photogrammetry and Remote Sensing (ASPRS) as a standardized format for lidar
data exchange and interoperability (Iqbal, 2023). Histogram charts of the GR curves were plotted and found to
be bimodal in the wells suggesting two predominant lithologies of sandstone and shale. Figure 4 shows a
graphical representation of the petrophysical workflow.
Figure 4: Graphical representation of Petrophysical workflow
Petrophysical properties Evaluation:
The petrophysical properties evaluauted in this work are discussed below:
Grain Density
Core data (conventional and sidewall Cores) were not available for this evaluation to carry out log-core
calibration. Average grain density value of 2.65 g/cc, which is the value for quartz matrix, was used for the
evaluation due to lack of core data. Andrea et al., (1997) opined that the value of the grain density taken
depends upon the lithology of the interval under question. Tamunobereton-ari et al., (2013) are of the view
that, in the absence of core data, grain density from part of the Niger delta can be used in the estimation of
petrophysical parameters such as acoustic velocity, compaction factor, porosity, permeability and fluid content.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4375
Determination of True Resistivity (Rt)
True resistivity is the value of deep resistivity reading from resistivity logs.
Water Resistivity Determination (Rw)
The formation water resistivities for the reservoirs were determined from the Pickett plot. 1.8 was used as the
cementation factor (m) and saturation exponent (n). Pickett plots were generated to determine the formation
water resistivity across clean water bearing zones. The water resistivity derived from Pickett plot in the water
leg was used for Water Saturation evaluation in all the available wells.
Formation water resistivity (Rw) was determined from a Pickett plot of porosity against resistivity over clean
water leg and wet zones. Shale resistivity was read from logs above and below reservoir intervals. Figure 5
shows the Formation Water Resistivity from Picket (Ani-1).
Figure 5: Formation Water Resistivity from Picket (Ani-1)
Rock Property Determination
Volume of Shale
Gamma ray logs were used to determine the volume of shale using the normalized gamma ray log. The
Larionov’s method for tertiary rocks with the equation shown below was used for estimating the volume of
shale.
Gamma Ray Method (Larionov for Tertiary rocks)
…………………………… (1)
…………………………………… (2)
Volumes of shale were calculated from gamma ray log after determining GR minimum and GR maximum
values for each zone in each well from a GR against frequency histogram plot and log display within, above
and below the reservoir intervals using the Gamma Ray index linear equation. Figure 6 shows Volume of
Shale estimated using Gamma Ray.
The sand and shale values were taken from the most representative intervals of well and an average volume of
shale cut-off 0.1 is adopted as reservoir.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4376
Figure 6: Volume of Shale estimated using Gamma Ray
Porosity
Porosity was determined from the density logs, Neutron and Sonic logs. Density of Matrix (RhoM) -
2.65g/cm
3
, water -1g/cm
3
, oil- 0.85 g/cm
3
and gas- 0.75 g/cm
3
were used as constants for density of water, oil
and gas respectively.
Total porosity (PHIT) and Effective porosity (PHIE) were estimated from Neutron and Density and Sonic
(Wyllie’s method). Logs used for each well were determined by availability; Density in Ani-1, Sonic in Ani-2
and Neutron-Density in Ani-3. A correction was applied on PHIE in Ani-1 to reduce overestimation of
porosity in gas zones. The estimated porosity porosity log across Animaux Field is shown in figure 7
Figure 7: Estimated porosity log across Animaux Field
Water Saturation
Water saturation was estimated using Archie’s method, effective porosity was the porosity used
………………………………………….. (3)
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4377
Figure 8 shows the estimated water saturation across Animaux Field.
Figure 8: Estimated water saturation across Animaux Field
Fluid Distribution
A combination of gamma ray, resistivity and porosity logs (neutron density) were used to distinguish reservoir
rocks from non-reservoir rocks. The reservoirs are made up of mainly sandstones, shale and sandstone with
shale intercalations based on log signatures interpretation.
Fluid interpretation was based on the resistivity logs while hydrocarbon typing was based on the combination
of neutron density logs. Fluid distribution plots were generated for all hydrocarbon-bearing reservoirs and are
presented in Figures 9-11.
Figure 9: Ani-1 Hydrocarbon Bearing (E, G, H- reservoir)
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4378
Figure 10: Ani-2 Hydrocarbon Bearing zones (K-reservoir)
Figure 11: Ani-3 Hydrocarbon Bearing zone (L-reservoir)
RESULTS AND DISCUSSIONS
The results of the reservoir characterization through the application of petrophysical evaluation of well logs
data of Animaux Field, Niger Delta Basin Nigeria are summarized in Tables 2 and 3.
Five reservoirs were identified as hydrocarbon-bearing and Fluid typing is based on combining information.
The sums and averages are presented in Table 2 and 3.
Table 2: Sums and Averages in MD
Well
Zone
Fluid
Top
Bottom
Gross
Res
Contact
Type
Contact
Depth
Gross
Pay
Net
Pay
NTGpay
Avg
Shale
AvgPor
AvgSw
Ani-
1
E
Oil
2721.93
2760.01
38.08
OWC
2727.80
5.639
3.048
0.541
0.125
0.28
0.319
Ani-
1
G
Oil
2833.88
2912.05
78.17
OWC
2866.64
8.077
7.62
0.943
0.036
0.279
0.206
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4379
Ani-
1
G
Gas
2833.88
2912.05
78.17
GOC
2858.56
24.536
11.278
0.46
0.094
0.285
0.203
Ani-
1
H
Oil
2945.59
2966.06
20.46
OWC
2960.67
3.505
3.048
0.87
0.013
0.281
0.262
Ani-
1
H
Gas
2945.59
2966.06
20.46
GOC
2957.16
11.43
8.992
0.787
0.1
0.289
0.192
Ani-
1
H_1
Gas
2996.18
3122.76
126.58
GWC
3021.93
25.603
9.906
0.387
0.128
0.284
0.161
Ani-
2
K
Oil
3466.08
3509.67
43.58
OWC
3473.80
7.62
0
0
0
0
0
Ani-
3
L
Oil
3616.36
3655.86
39.49
OWC
3631.26
14.903
7.315
0.491
0.159
0.242
0.272
Table 3: Sums and Averages in TVDSS
Well
Zone
Fluid
Top
Bottom
Gross
Res
Contact
Type
Contact
Depth
Gross
Pay
Net
Pay
NTGpay
Avg
Shale
AvgPor
AvgSw
Ani-
1
E
Oil
2707.89
2745.97
38.08
OWC
2713.63
5.63
3.048
0.541
0.125
0.28
0.319
Ani-
1
G
Oil
2819.85
2898.02
78.17
OWC
2852.47
8.07
7.62
0.943
0.036
0.279
0.206
Ani-
1
G
Gas
2819.85
2898.02
78.17
GOC
2844.39
24.53
11.278
0.46
0.094
0.285
0.203
Ani-
1
H
Oil
2931.56
2952.03
20.47
OWC
2946.50
3.50
3.048
0.87
0.013
0.281
0.262
Ani-
1
H
Gas
2931.56
2952.03
20.47
GOC
2942.99
11.4
8.992
0.787
0.1
0.289
0.192
Ani-
1
H_1
Gas
2982.15
3108.74
126.58
GWC
3007.76
25.60
9.906
0.387
0.128
0.284
0.161
Ani-
2
K
Oil
3452.35
3495.9
43.59
OWC
3459.90
7.55
0
0
0
0
0
Ani-
3
L
Oil
3600.41
3638.61
35.71
OWC
3617.75
13.42
6.586
0.491
0.159
0.242
0.272
E Reservoir
The E reservoir penetrated by Ani-1, -2 and -3. The reservoir is oil bearing only in Ani-1 penetrating the
structure at a depth of -2707.89m (TVDSS) with oil water contact at -2713.63m (TVDSS). E-reservoir has an
average gross thickness of 5.63m, a net thickness of 3.05m, an average net-to-gross ratio of 0.54; average
porosity of 28% and average water saturation of 32%.
G Reservoir
G reservoir penetrated by Ani-1, -2 and -3. The reservoir is both oil and gas bearing in Ani-1. For the oil, Ani-
1 has a penetrating depth -2819.85m (TVDSS) with a GOC at -2844.39m (TVDSS) and OWC at -2852.47m
(TVDSS). G-reservoir has an average gross thickness of 8.07m, a net thickness of 7.62m, an average net-to-
gross ratio of 0.94, average porosity of 28% and average water saturation of 21% in the oil zone and an
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4380
average gross thickness of 24.53m, a net thickness of 11.28m, average net-to-gross ratio of 0.46, average
porosity of 29% and average water saturation of 20% for the gas zone.
H Reservoir
H reservoir penetrated by Ani-1, -2 and -3. The H reservoir has one oil zone and two gas bearing zone in Ani-1
denoted as H and H_1 For the oil Ani-1 has a penetrating of depth -2931.56m (TVDSS)with oil water contact
at -2946.50m (TVDSS). H-reservoir for oil has an average gross thickness of 3.50m, a net thickness of 3.05m,
an average net-to-gross ratio of 0.87, average porosity of 28% and average water saturation of 26%.Ani-1
penetrates gas in zones H and H_1 at depths of -2931.56m (TVDSS) and 2982.15m (TVDSS) respectively and
with gas oil contact at -2942.99m(TVDSS) for H gas zone and gas water contact of -3007.76m (TVDSS) for
the H_1 zone. H and H_1 reservoirs for gas have an average gross thickness of 11.4m and 25.60m, net
thicknesses of 8.99m and 9.91m , an average net-to-gross ratios of 0.79 and 0.39; average porosities of 29%
and 28% and average water saturations of 19% and 16% respectively.
K-Reservoir
The K reservoir is basically oil bearing only at Ani-2 well. The reservoir penetrates the structure at a depth of -
3452.35m(TVDSS)with oil water contact at -3459.90m(TVDSS). K-reservoir has an average gross thickness
of 7.55m.
L-Reservoir
The L reservoir penetrated by Ani-2 and -3. The reservoir is oil bearing only in Ani-3 penetrating the structure
at a depth of -3600.41m (TVDSS) with oil water contact at -3617.75m (TVDSS). L-reservoir has an average
gross thickness of 13.42m, a net thickness of 6.59m, an average net-to-gross ratio of 0.49; average porosity of
24% and average water saturation of 27%.
Ani-1 penetrated most of the hydrocarbon bearing reservoirs in the field, three oil zones ( E, G and H) and
three gas zones (G, H and H_1 ).
Comparing Results with other works in the Niger Delta
The average porosity of 28% in the Animaux Field compares favourably with the porosity range for the Niger
Delta basin as reported by Opara (2010) in eight reservoirs of USSO Field Onshore Niger Delta Basin –who
had 27% porosity. Tamunosiki et al., (2014) in the south-east Niger Delta Basin had porosity values that
ranged from 15% to 31% and Oluwajana and Owoeye (2023) reported an average porosity of 27% for the
XYZ Field in the Niger Delta Basin. These show that the estimated grain density of 2.65g/cc was within a
geological plausible range. This has taken away data gaps which are usually associated with lack of core data
and bridged any element of uncertainty which could have resulted from estimated grain density.
The reservoirs of Animaux Field are made up of mainly sandstones, shaly sand and sandstone with shale
intercalation based on the log signature which is typical of the Agbada Formation of the Niger Delta as
reported by Reijers et al., 1997; Doust and Omatsola, 1990 and which is consistent with the work of
Emujakporue, (2016) in Amu Field of the Niger Delta who delineated sand and shale as the two major
lithologies from well logs. Generally, the identification of five reservoirs E, G, H, K, and L as hydrocarbon
bearing in theAnimaux Field, goes to support the works of Oyeyemi et al. (2018) within an exploration Field,
Shallow Offshore Depobelt, Western Niger Delta, Nigeria; and Ola and Alabere, (2018) in the OVU Field,
onshore Niger Delta and James(2021) in Honyx Field, Niger Delta; that the application of Petrophysical
evaluation of well logs can be used in reservoir characterization.
CONCLUSION
The reservoir characterization through the application of petrophysical evaluation of well logs data was carried
out in Animaux Field, Niger Delta basin Nigeria using data from three wells. Checkshot data from Ani-1 well
and a suite of well logs from three wells (Ani-1, Ani-2, and Ani-3) were used for the evaluation. The
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4381
Petrophysical analysis produced an average porosity value of 28% for the Ani-1 well for the various zones and
water saturation of 31%, 21% and 20% for the E,G,and H oil zones respectively. Reservoirs E, G, H, and L have
an average net pay thickness of 3.05m, 7.62m, 3.05m and 6.59m for the oil zones respectively, while the net
pay thickness for reservoirs G, H and H_1 gas zones are 11.28m, 8.99m and 9.91m respectively
Five hydrocarbon reservoirs containing oil, in three levels and oil and gas in two levels were interpreted from
the three wells and evaluated for their respective petrophysical properties (net-to-gross, thicknesses, water
saturation and porosity). The results generally show that five reservoirs E, G, H, K, and L were identified as
hydrocarbon bearing. The reservoirs are made up of mainly sandstones, shaly sand and sandstone with shale
intercalation based on the log signature. The reservoir parameters obtained show that the reservoirs are good
and of high quality.
REFERENCES
1. Adagunodo, T. A and Akinlabi, I. A., (2024): Petrophysical analysis to determine the hydrocarbon
prospectivity of sands in AA field, Niger Delta.IOP Conference Series Earth and Environmental
Science 1342(1):012042
2. Amangabara, G andObenade, M, (2015): Flood Vulnerability Assessment of Niger Delta States
Relative to 2012 Flood Disaster in Nigeria. DOI:10.12691/env-3-3-3
3. Andrea, M., Sams, M. S., Worthington, M. H., and King, M. S., (1997):Predicting Horizontal
Velocities from Well Data, Geophysical Prospecting,45: 593 – 609.
4. Asquith, G. and Krygowski, D. (2004) Basic Well Log Analysis. AAPG Methods in Exploration
Series, No. 16, 2004.
5. Corredor, F., Shaw, J. H. and Biolotti F. (2005): Structural Styles in the deep-water fold and thrust of
the Niger Delta.AmAssoc Pet Geo Bull 89:753-780.
6. Doust, H. and Omatsola, E.M. (1990): Niger Delta in: Divergent/passive Margin Basin. D.Edward and
P.A Santagrossi (eds). AAPG Memoir and 45: Oklahoma. 201-238
7. Emujakporue, G. O (.2016): Evaluation of Hydrocarbon Prospect of Amu Field, Niger-Delta, Nigeria.
International Research Journal of Geology and Mining (IRJGM) (2276-6618) Vol. 6(1) pp. 001 – 008
8. Evamy, B.D; Haremboure, J; Kamerling, P; Knaap, WA; Molloy, F.A; and Rowlands, P.H; (1978):
Hydrocarbon habitat of Tertiary Niger Delta: Am. Assoc. Petrol. Bull. 62:277-298.
9. Ganguli, S.S., Dimri, V.P., 2023. Reservoir characterization: State-of-the-art, key challenges and ways
forward. In: Ganguli S.S., Dimri V.P. (eds) Reservoir Characterization, Modeling and Quantitative
Interpretation: Recent Workflows to Emerging Technologies
10. James, A. S. (2021): Hydrocarbon Prospectivity Evaluation of ‘Honyx’ Field, Niger Delta.
International Journal of Scientific Research and Engineering Development-– Volume 4Issue 6.
11. Larionov, V.V. (1969) Borehole Radiometry Moscow, U.S.S.R. In: Nedra, M.R.L. and Biggs, W.P.,
Eds., Using Log-Derived Values of Water Saturation and Porosity, Trans. SPWLA Ann. Logging
Symp. Paper, 10, 26.
12. Lawrence, S.R.,Munday, S.,Bray (2002):Regional Geology and Geophysics of the Eastern Gulf of
Guinea (Niger Delta to Rio Muni):The Leading Edge 21(11):1112-1117.
13. Najime,T; Eduvie,M.O; Jolly,B; (2017,May 25): Evolution of the Niger Delta, present dynamics and
future. Ajol. https://www.ajol.info/index.php/sa/issue/view/15819
14. Ola, P. S., &Alabere, A. (2018). Reservoir Characterization and structural mapping on OVU Field,
Onshore Niger Delta using well logs and 3-D seismic data. International Journal of Scientific and
Engineering Research, 5(6), 17-24
15. Oluwajana, A.O., and Owoeye, O.J., (2023): Integrated Prospectivity Evaluation of XYZ Field
Coastal-Swamp Depobelt Niger Delta Basin,Nigeria.Asian Journal of Applied Science and Technology
(AJAST) Volume7, Issue 4, Pages 01-12, October-December, 2023
16. Opara, A. I., (2010): Prospectivity Evaluation of “USSO” Field, onshore Niger delta basin,using 3-D
seismic and well data Petroleum & Coal 52 (4) 307-315.
17. Oyeyemi, K., Olowokere, M. and Aizebeokhai, A.P., (2018):Prospect Analysis andHydrocarbon
Reservoir Volume Estimation in an Exploration Field, Shallow Offshore Depobelt, Western Niger
Delta, Nigeria.Natural Resources Research 28(11) DOI:10.1007/s11053-018-9377-4
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4382
18. Lehner, P., and De Ruiter, P.A.C., (1977): Structural history of Atlantic Margin of Africa: American
Association of Petroleum Geologists Bulletin, v. 61, p. 961-981.
19. Reijers, T.J.A., Petters, S.W., and Nwajide, C.S., (1997): The Niger Delta Basin, in Selley, R.C., ed.,
African Basins--Sedimentary Basin of the World 3: Amsterdam, Elsevier Science, pp. 151-172
20. Short, K.C. and Stauble, A.J., (1967): Outline geology of the Niger Delta. American Association of
Petroleum Geologists Bulletin 51, 761–779.
21. Tamunobereton-ari, I., Uko, E. D. and Omubo-Pepple, V. B., (2013): Estimation of lithological , and
mineralogical contents of rocks from Matrix density in part of Niger Delta Basin Nigeria using Well-
log Data. Journal of Emerging Trends in Engineering and Applied Sciences(JETEAS) 4(6): 828-836 ©
Scholarlink Research Institute Journals, 2013 (ISSN: 2141- 7016) jeteas.scholarlinkresearch.org
22. Tamunosiki, D., Ming, G. H., Uko, E.D., Tamunobereton-ari, I.and Emudianughe, J.E., (2014):Porosity
modeling of the south-east Niger Delta Basin .Nigeria. International Journal of Geology, Earth and
Environmental Sciences ISSN: 2277-2081 (Online) An Open Access, Online International Journal
Available at http://www.cibtech.org/jgee.htm 2014 Vol. 4 (1) January-April, pp.49-60/Tamunosiki et
al.
