# Assessment of Economically Accessible Groundwater Reserve and Its Protective Capacity in Eastern Obolo Local Government Area of Akwa Ibom State, Nigeria, Using Electrical Resistivity Method

Authors: N. J. George; E. U. Nathaniel; S. E. Etuk
Journal: ISRN Geophysics
 (2014)

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## Abstract

The application of geophysical method employing vertical electrical sounding (VES) method in combination with laboratory analysis of aquifer sediments has been used to access the economically accessible groundwater reserve and its protective capacity in some parts of Eastern Obolo Local Government area, the eastern region of the Nigerian Niger Delta. Schlumberger electrode configuration was used to sound twelve VES to occupy the areas that have borehole locations and accessibility for the spread of current electrodes to at least 1000 m. Based on the results, the safe and economic aquifer potential has groundwater reserve of about168480558±18532861 m3. The desired aquifer thickness and its depth of burial have average value of 52.02 m and 73.14 m, respectively. The area has a fair protective capacity. This is indicated by 58.33% weak, 16.67% moderate, and 25% good protective capacity for the area. This study was done in one of the oil cities, where contaminated Salt River water is used as the major source of water for domestic uses and it is believed that the settlers will appropriate this result and sue for safe groundwater at the indicated depths.


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## Body

## 1. Introduction

The need to assess safe groundwater repositories is increasingly important in Eastern Obolo Local Government Area (EOLGA), located in Eastern Niger Delta region of Nigeria, because of the extant health hazards posed by surface and underground contaminations which are associated with chemical, microbial, and physical contaminant plumes. The focus of groundwater quality protection is on the prevention of groundwater pollution; however, where groundwater has become polluted, it must be cleaned up and managed to ensure the ongoing protection of human health and the environment. Access to clean water is a human right and basic requirement for economic development. The worldwide development of past civilisations as well as the recent socioeconomic evolution of nations is based on and strongly controlled by the availability of water which can be obtained either as surface or subsurface water.The Niger Delta region (the site for which the study area is located) is one of the most industrialised parts in the entire Gulf of Guinea. These industries have contributed immensely to economic growth and development of the states within the region. The region which is one of the ten most important wetland and marine ecosystems in the world has suffered immensely from these unsustainable industrial activities [1]. Some of the human activities that have impacted negatively on the Niger Delta environment include industrial (hydrocarbon exploration and exploitation, noise pollution, gas flaring, oil spillage, and waste disposal), removal of backshore vegetation, construction of barges and other coastal control works, river dredging, agricultural (excessive and uncontrolled application of inorganic fertilizers, pesticides, and herbicides), municipal waste disposal, urbanisation, and mining activities [2, 3]. Environmental problems like loss of biodiversity, coastal and riverbank erosion, flooding, land degradation, loss of soil fertility, and deforestation are now common [1, 4]. Most of the numerous surface water resource endowments of the area which the entire population used to be solely dependent on for their domestic, agricultural, industrial, and social needs are currently seriously polluted by both natural and anthropogenic sources [4]. Recently, the activities of criminals who are involved in illegal pipeline vandalism and crude oil theft and refining have compounded the environmental problems in the Niger Delta region. The quality of the groundwater resources in many parts of the region is gradually being degraded [5]. There is urgent need to understand the extent of natural protection, the distributions of the economically safe aquifer repository, and possibly the pollutant flow path in order to design appropriate mitigation, remediation, and protection strategies especially now that such plans are in the drawing board by governmental, environmental, and other international partners. Design of appropriate groundwater management strategies in any geologic environment depends to a reasonable extent on the nature of subsurface materials whose properties (physical and chemical) and spatial distribution constitute the goal of all hydrogeological and hydrogeophysical investigations. The surface water is found to be grossly degraded in quality because of its exposure to physical, biological, or chemical contaminants [6]. Groundwater on its own has less degree of contaminations when relatively compared with surface water. Inadequate public water supply has led to increased demand for alternative sources of water supply in EOLGA due to rapidity in human growth in recent times [7]. Today, we are witnessing the increasing number of boreholes drilled by the government, nongovernmental organizations, and individuals. This clearly shows that groundwater is effectively complementing other sources of water supply in the area. This is due to the rate of contamination of surface water through the effect of inordinate quest for development. Wildcat drilling, which does not support any scientific search for the location of the water bearing sediments in the area, has exposed many groundwater resources to contaminations. In view of this, the area was mapped in order to access the quantitative reserve, distribution of the safe and economic water bearing units, its depth of burial, and the protective capacity of the groundwater repositories in the study area.


## 2. Location and Geology of the Study Area

The study area shown in Figure1 lies between longitudes 7°30′ and 7° 42′ E and latitudes 4°15′ and 4°32′ N in the Niger Delta region of Southern Nigeria. The study was designed to cover some areas of Eastern Obolo Local Government Areas of Akwa Ibom State of Nigeria, one of the oil rich regions of the Arcuate Niger Delta, surrounded by both fresh and saline water. The study area is located in an equatorial climatic region that is characterised by two major seasons. The seasons are the rainy season (March–October) and dry season (November–February) [7, 8]. The dry season is a period of extreme aridity characterized by excruciating high temperatures that do reach 35°C. The area has been severely affected by the current global climatic changes in such a way that there have been shifts in both the upper and lower boundaries of these climatic conditions [9–13]. The study area is located in the tertiary to quaternary coastal plain sands (CPS) (otherwise called the Benin Formation) and alluvial environments of the Niger Delta region of Southern Nigeria (Figure 1). The Benin Formation which is underlain by the paralic Agbada Formation covers over 80% of the study area. The sediments of the Benin Formation consist of interfringing units of lacustrine and fluvial loose sands, pebbles, clays, and lignite streaks of varying thicknesses, while the alluvial units comprise tidal and lagoonal sediments and beach sands and soils [14–16] which are mostly found in the southern parts and along the river banks. The CPS is covered by thin lateritic overburden materials with varying thicknesses at some locations but is massively exposed near the shorelines. The CPS forms the major aquiferous units in the area. It comprises poorly sorted continental (fine, medium, and coarse) sands and gravels that alternate with lignite streaks, thin clay horizons, and lenses at some locations. The thin clay/shale horizons truncate the vertical and lateral extents of the sandy aquifers thereby building up multiaquifer systems in the area [17]. Thus both confined and partially confined aquifers can be found in the area. Southward flowing rivers like the Kwa River and their tributaries that empty directly into the Bight of Bonny drain the area.Figure 1
Location map showing the study location, VES points and VES traverses.


## 3. Data Acquisition and Analysis

Surface electrical methods have been used in investigating different types of geological, geotechnical, and environmental problems for many years due to the dependence of earth resistivity on some geologic parameters. Generally, the electrical resistivity method involves injecting electrical current into the ground through a pair of electrodes (called current electrodes) and monitoring the potential difference created by the passage of the electrical current through the earth materials using another pair of electrodes technically called potential electrodes. Details of this can be found in [18–20]. Many electrode configurations and field procedures exist that can be used to perform geoelectrical investigations [18], but the Schlumberger array which was performed using the vertical electrical sounding field procedure was adopted to assess the subsurface electrical resistivity and the depth of the economically accessible aquifers. The Schlumberger electrode configuration used in measuring apparent resistivity ρa is shown in the following equation:
(1)ρa=π·((AB/2)2-(MN/2)2(MN))·Ra,
where AB is the distance between two current electrodes, MN is the distance between two potential electrodes, and Ra is the apparent electrical earth’s resistance measured from the equipment. The term in the equation shown below is called the geometric factor (G):
(2)G=π·((AB/2)2-(MN/2)2MN).
The electrical resistivity investigations were conducted in twelve (12) locations across the study area between 2011 and 2012 using a SAS1000 model of ABEM Terrameter. Maximum current electrode spacing (AB) was constrained by settlement pattern and other space limiting conditions to vary from one location to another. This confined the VES points to the locations shown in Figure 1. In locations with good access paths and/or roads, the current cables were extended up to 1000 m in order to ensure that depths above 200 m were comfortably sampled assuming that penetration depth varies between 0.25 AB and 0.5 AB [21, 22]. Corresponding receiving (potential) electrode separation (MN) varied from a minimum of 0.5 m at AB = 2 m to a maximum of 50 m at AB = 1000 m. At all the electrode positions, great care was taken to ensure that the separation between the potential electrodes did not exceed one-fifth of the separation of the current electrodes [23]. VES data quality was generally good especially in the wet season, but, in the dry season, the electrode positions were usually wetted with water and salt solution (where necessary) in order to lower the contact resistance and consequently ensure good electrical contact between the ground and the steel electrodes.Information generated from the analyses of geophysical data was used to constrain drilling. The drilling phase started as soon as the geophysical reports were submitted to the Akwa Ibom State Millennium Development Goal [24] that funded the borehole projects. Manual drilling technique was adopted in drilling 6-inch borehole in all locations since the subsurface condition in the entire area was avourable to such drilling method. Some of the boreholes were cited adjacent to the VES stations while some that are located at where there was no access path to spread the cables at the vicinity of the boreholes were separated by more than 150 m (see Figures 2, 3, and 4). The drill cuttings were logged geologically in all 12 locations and from the desired aquifer samples were collected for laboratory measurement of porosity. The drilled boreholes were cased using 75 mm high pressure PVC casing materials. The PVC casings were slotted at various depths with sizable thicknesses and the slotted region of the well annulus was gravel packed to ensure good delivery of water to the borehole. Gravel packing is also important in checking the ingress of sediments into the borehole. A mixture of sand and cement was used to grout the boreholes to prevent backflow of water at the surface into the well [25]. The wells were developed.Samples of modelled VES curves alongAB profile showing correlations between VES derived 1-D subsurface models and borehole lithologs.
(a)
(b)Samples of modelled VES curves along CD profile showing correlations between VES derived 1-D subsurface models and borehole lithologs.
(a)
(b)Samples of modelled VES curves along EF profile showing correlations between VES derived 1-D subsurface models and borehole lithologs.
(a)
(b)The core samples were prewashed with distilled water to remove traces of clay and other argillaceous materials that might have originated from the coring operation [26]. The samples were later put into a vacuum desiccator and evacuated at a pressure of 0.3 mBar for a period of 1 hour (see [27]). Deaerated distilled water was gently poured into the desiccator until the water completely covers all the samples. All the samples were later soaked for a period of 24 hours in order to ensure that any trace of salt and other related soluble contaminants within the samples diffused out into the surrounding water. The cleaned samples were later dried in a temperature controlled oven at 105°C for 16 hours in order to check any irreversible change in the composition of the samples (see [27, 28]). The oven dried core samples were allowed to cool to normal air temperature in a desiccator. The weight of the cool and dry core samples Wd was measured using an electronic weighing balance five times and the mean was computed and recorded. The samples were soaked with distilled water that has been boiled for 30 minutes in a vacuum pressure of 0.3 mBar for 18 hours. The weight of the wet samples Ww was also measured five times and the mean was computed and recorded. Effective porosity (ϕ) of the samples was calculated using (6) as
(3)ϕ=100·(Ww-WdV)%,
where V is volume of the samples. Details of the experimental procedure can be found in [26–28].


## 4. Results and Discussion

The field data consisting of the apparent resistivity(ρa) and the current electrode spacing (AB/2) were partially curve matched, smoothened, and manually plotted against each other on a bilogarithmic scale with ρa on the ordinate and current electrode AB/2 on the abscissa. It was performed by either averaging the two readings at the crossover points or deleting any outlier at the crossover points that did not conform to the dominant trend of the curve. Also deleted were data that stood out as outliers in the prevalent curve trend which could have caused serious increase in root mean square error (RMSE) during the modelling phase of the work. Where observed, such outliers constitute less than 2% of the total data generated in each sounding station and since we measured over ten data per decade, deleting such noisy data did not alter the trend of the sounding curve. Some of the deleted data might have been the electrical signatures of the thin clay materials that might have suffered suppression from the over- and underlying thick sandy aquifers [29, 30]. Any discontinuity observed in the smoothened curves was exclusively attributed to vertical variation of electrical resistivity with depth. Preliminary interpretation of the smoothened curves was made using the traditional partial curve marching technique to estimate primary layer parameters. The partial curve matching technique uses master curves and charts developed by [31]. A computer based VES modelling software called RESIST [32] that can perform automated approximation of the initial resistivity model from the observed data was later used to improve upon the preliminary interpreted results using the inversion technique. The RESIST software uses the initial layer parameters to perform some calculations and at the end generates a theoretical curve in the process. It then compares the theoretical curve with the field data curve. Since quantitative interpretation of geoelectrical sounding data is usually difficult due to the inherent problem of equivalence [33], borehole data were used to constrain all depth and minimise the choice of equivalent models by fixing layer thicknesses and depths while allowing the resistivities to vary [34]. The total number of observed minima and maxima on the smoothened VES curves was usually used as the starting number of layers (or models) over a half-space for the data inversion exercise. The software works iteratively by calculating at the end of each step updated parameters of the model and calculates the extent of fit between the calculated and the observed data using the root mean square error (RMSE) technique in which 5% was preset as the maximum acceptable value. Figures 2, 3, and 4 show some of the modelled VES curves observed and their correlation between the nearby borehole lithology and interpreted results. A good correlation was observed between the borehole lithology log data and the inverted results over half-space in many locations. These noticed distortions were suspected to have possibly originated from the failure of the 1D assumption of the shallow subsurface of the half-space [29]. The result of the computer iterations gave the layers of the subsurface penetrated by current, true resistivity of each layer, the thickness (h), depth (d) of each layer, and the total depth of overburden to the safe and economically accessible auriferous layers as shown in Table 1. Three to four layers with different curve types were delineated. The availability of H and K curve types indicates the high and low values of resistivities in sediments which translate from unsaturated zones into saturated zones. Specifically, the curve types in the areas occupied by VES are KH which takes 41.7% K, H, AH, AK, and QH which, respectively, take 8.3% of the total curve distributions. The other curve type also noticed is the AH curve type which takes 16.7% of the curve distributions (see Table 1 and Figure 5 for curve distributions). From the inferred layer resistivities and thicknesses, the longitudinal conductance known as one of the Dar Zarrouk parameters was used to classify the degree of protection of each of VES sites according to rating in Table 2 into weak, moderate, good, and excellent aquifers based on the numerical values assigned to each point. Based on the longitudinal conductance, the protective capacity of the overburden showed that 58.33% of the mapped area has weak protective capacity, 16.67% moderate protective capacity, and 25% good protective capacity. This is an indication that the chosen economic aquifer repository apart from having a sizeable thickness of average value of 52.05 m and average depth of 73.14 m also has a fair protection from the surface flow despite its open nature. The sizeable thickness and the average depth of the aquifer located in aquifer units ranging from fine sand to coarse or in siltstone can be economically exploited for domestic and industrial uses by the settlers of the region.Table 1
Summary of location coordinates, geoelectrical layer properties and protective strength of the study area.
VES number
Latitude (degree) y (m)
Longitude (degree) x (m)
Bulk resistivity of layers from top to bottom of the economic aquifer (Ωm)
Thickness of  layers from top to  bottom  of economic aquifer (m)
Depth to economic aquifer  (m)
Longitudinal conductanceS (Ωm)
Economicaquifer fractional porosity
Protective strength
Curve type
ρ
1
ρ
2
ρ
3
ρ
4
h
1
h
2
h
3
1
2982
176
660.4
1488.6
549.2
1248.7
1.3
3.6
47.8
52.7
0.09
0.305
Weak
KH
2
2828
357
418.6
1913.0
392.1
—
1.7
112.3
—
114.0
0.06
0.350
Weak
K
3
2836
607
1304.5
720.2
2049.4
—
1.2
15
51.2
67.4
0.05
0.278
Weak
H
4
2882
794
949.9
2715.5
1464.5
3745.2
0.5
3.9
16.1
20.6
0.01
0.301
Weak
KH
5
1850
1009
718.4
1844.9
1362.2
1440.8
0.5
1.6
38.9
82.9
0.03
0.280
Weak
KH
6
1423
210
1433.0
210.0
860.5
8367.3
19.4
43.1
49.6
112.4
0.28
0.260
Moderate
HA
7
1950
1328
205.8
750.3
52.6
66.8
2.9
11.8
68.2
82.9
1.33
0.195
Good
KH
8
1363
1586
1826.8
107.5
40.1
64.7
3.6
24.6
45.2
73.4
1.36
0.312
Good
AH
9
581
104
3455.5
1825.6
393.9
2030.4
0.6
2.0
89.2
91.8
0.23
0.276
Good
QH
10
9562
150
210.7
397.9
1721.4
228.2
0.7
12.8
55.2
68.7
0.07
0.256
Weak
AK
11
9740
209
1335.1
3372.5
924.2
1681.6
4.4
55.6
50.9
110.9
0.07
0.289
Weak
KH
12
1867
356
1721.4
118.2
611.2
4893.0
91.6
56.7
36.8
115.1
0.59
0.406
Moderate
HATable 2
Protective capacity ratings of longitudinal conductance.
Range
Strength
S
>
10
Excellent
5
<
S
<
10
Very good
0.7
<
S
<
4.9
Good
0.2
<
S
<
0.69
Moderate
0.1
<
S
<
0.19
Weak
S
<
0.1
PoorFigure 5
Bar chart showing frequency of curve type distribution in the study area.The longitudinal conductance (S) is given by
(4)S=∑i=1nhiρi=h1ρ1+h2ρ2+h3ρ3+···+hnρn,
where hi and ρi are the saturated thickness of each of the layers and their corresponding true resistivities, respectively. The earth’s medium acts as a natural filter to percolating fluid. The ability of the earth to retard or accelerate and filter percolating fluid is a measure of its protective capacity [35, 36]. The total longitudinal conductance is a parameter used to define the target areas of groundwater potential. High S values indicate relatively thick geologic succession and should be accorded with the highest priority in terms of groundwater potential while low S reflects thin geologic successions with low groundwater potential [35]. The contour map showing S distribution is given in Figure 6.Figure 6
Distribution of longitudinal conductance map showing high values ofS in VES 6, 7, and 8 in the north-eastern zone of the mapped area.The results of the thickness and total depth of groundwater repository in Table1 were, respectively, used in generating the 2D and 3D representation of the subsurface economic groundwater repository and 3D map showing the total depth in a continuum (see Figures 7(a), 7(b), and 8). Applying SURFER programme to the thickness geometry, the total volume of the unconfined aquifer geometry was computed as 576396025 m3and the porous volume based on the average porosity of the unconfined aquifer of 29.23% (0.2923) was 168480558 m3 using the latitude of each of the survey lines as the y-axis and the longitude as x-axis. The porosity distribution was also produced using fractional porosities in Table 1 and the porosity variation is shown in Figure 9. These maps describe the variations of the aquifer and can explain the flow pattern of groundwater. The thickness of the economic and safe aquifer repository to the estimated depth was considered as thez-axis. The error in porous volume was computed using the equation below:
(5)ΔVV=Δll+Δww+Δhh+Δϕϕ,
where ΔV, Δl, Δh, Δw, ϕ, and Δϕ are error in volume V, error in length l(0), error in thickness h(0), error in width w, and error in porosity. The error in length and width is negligible. Hence,
(6)ΔVV=0+0+0.1+.001=0.11(7)ΔV=V·0.11=168480558·0.11=18532861m3.
Therefore, the approximate volume of porous zone is 29.23% of the considered aquifer total volume. Hence, there exists 168480558±18532861 m3 of water residing in the study area.(a) 2D distribution of aquifer thickness in the mapped area. (b) 3D distribution map of the preferred aquifer thickness in the mapped area used in computing the groundwater reserve.
(a)
(b)Figure 8
3D distribution map of depth to aquifer in the mapped area.Figure 9
3D distribution map of porosity of the preferred aquifer in the mapped area.


## 5. Conclusion

The inferred safe and economic aquifer repository located in fine to gravelly sands does not show any interaction in the reference location based on the resistivity, the converse of conductivity. The estimated reserve shows that more boreholes can be drilled to exploit the described aquifer so that the reasonable population who relies on the Salt River water in the area can avoid the biological, chemical, and physical adverse consequences associated with river water. The protection of the aquifer based on the overburden materials is commendable as the area has, on the average, good aquifers mostly within the vicinity of VES 6–12. Laboratory checks can be conducted from time to time in order to access the protective capacity of aquifers within the regions of VES 1–5 described as weak. The average depth of the overburden to the aquifer repository, the highly resistive topsoil, and estimated protective capacity on the average indicate that the safe economically recommended aquifer can be free from surface or near-surface contaminant plumes. The porosity, longitudinal conductance, thickness, and depth to aquifer distribution maps drawn could be used to derive input parameters for contaminant migration modelling and to improve the quality of model. The method is unique as the calculated aquifer parameters are well defined within the range of observed aquifer parameters.



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*Source: 578981-2014-04-14.xml*