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Correlation of Schlumberger Array Geoelectric Log with Borehole

Lithologic Log in Ekiti State University Campus, Ado Ekiti, Ekiti

State, Nigeria.

Ojo O. F.

, Ujah S. A.

, Murana K. A.

, Ogunlana F. O.

, Faleye E. T.

and Adeniran, M. A.

Department of Geology, Ekiti State University, Ado‑Ekiti, Nigeria

Department of Geological Sciences, Federal University Gusau, Zamfara State, Nigeria

Department of Physics, Bamidele Olumilua University of Education, Science and Technology, Ikere-Ekiti, Nigeria

Department of Physical and Chemical Sciences, Elizade University, Ilara-Mokin, Ondo State, Nigeria.

*Corresponding Author

DOI : https://doi.org/10.51583/IJLTEMAS.2024.130510

Received: 24 March 2024; Revised: 03 May 2024 Accepted: 08 May 2024; Published: 11 June 2024

Abstract: Correlation of the schlumberger array geoelectric logs interpreted from Vertical Electrical Sounding (VES) data

acquired in Ekiti State University Campus with borehole litholigic logs from the same area was done with the aim of establishing

a mathematical relationship between the two logs. The study area is located within the basement complex of southwestern

Nigeria, where occurrence of groundwater is limited to specific localized and enclosed regions within the weathered or fractured

zones. Therefore, thorough geophysical surveys before drilling become necessary. The availability of borehole data in this area

reduces the risk of data interpretation errors and enables prediction through statistical analysis. The Vertical Electrical Sounding

data acquired from five stations within the campus were processed and interpreted using WINRESIST interpretation software.

The results from the 2-D interpretation of the VES data were used to prepare five geoelectric sections which were compared with

the borehole litholigic logs obtained from the same VES points using regression analysis. Depth to bedrock from Geoelectric log

was correlated with the depth to bedrock from Lithologic log. The correlation coefficient gave a positive value of 0.9, which

indicated a high degree of correlation between the geoelectric log and the borehole logs. The regression analysis equation

obtained was y = 1.14x + 2.48. The equation is useful in converting the geoelectric logs to drilling logs in any location within the

study area.

I. Introduction

Electrical resistivity method involves introducing artificially-generated electric currents into the ground and measuring the

resulting potential differences at the surface with the aim of determining the distribution of electrical resistivity within the

subsurface. Electrical resistivity data acquisition either in the form of vertical electrical sounding or electric mapping has been in

vogue in Nigeria for long but precision in the interpretations of such data have been taking for granted (Olasehinde and Taiwo,

2000). The electrical resistivity method, employing vertical electrical sounding (VES) technique, is increasingly being used in

geophysical investigations related to environmental studies, groundwater exploration, and engineering activities (Afolayan et.al.,

2004; Abubakar and Auwal 2012; Adepelumi et al., 2013; Ochuko 2013; Okogbue and Omonona 2013; Oladunjoye et al., 2013;

Akande et al., 2016; Bienibuor et al., 2016; Kumar et al., 2016; Nicholas et al., 2016), especially in basement complex terrain

where groundwater prospecting can be very challenging due to the complex nature of the geological formations (Wannamaker et

al., 2016; Sunmonu et al., 2018).

The electrical resistivity method is significantly useful in studying hidden subsurface structures and rock types (Ojo et al., 1990

and Olayinka, 1996); however, much work has not been done in comparing the interpreted results with drilling logs (Ojo and

Ademilua, 2013). This could be as a result of lack of follow-up drilling exercises due to economic reasons (Olasehinde and

Taiwo, 2000). The determination of the litholigic layer from geoelectric data is not direct since the immediate aim of electrical

prospecting is the study of the geoelectic layer. It is necessary to reconstruct the geoelectric layer from the interpreted resistivity

data, utilizing well-established relations for a given region, thus effecting the transformation from geoelectic layer to lithologic

layer. The use of borehole data to complement the electrical resistivity data in delineating subsurface lithology reduces the

possibility of interpretation errors that relying solely on VES data could cause (Claris et al., 2022)

The present study is aimed at correlating the Schlumberger array geoelectric logs interpreted from VES data using iterative

computer modeling with borehole litholigic logs and to determine the mathematical relationship between the two for the purpose

of making predictions about the subsurface lithology within the area of study. In this work, geoelectic sections and borehole

lithologic sections were generated for five resistivity stations within the Ekiti State University campus.

II. Location and Geology of the Study Area

Ekiti State University which is the study area is located in Ado Ekiti along Iworoko road in Ekiti Srate (Figure 1). Ado Ekiti is

located between latitude 7

Ꞌ

and 7

Ꞌ

N and longitude 5

Ꞌ

and 5

Ꞌ

E, Southwest, Nigeria on a low-land surrounded by

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several isolated hills and inselbergs. The study area is located within the Basement Complex made up of the Precambrian

Crystalline Rocks which forms the rocks of the South Western Nigeria and consisting mainly of gneisses, migmatites and granites

and vast areas of Schists, Phylites and Quartzites and more occasionally Amphibolites, Diorites, Gabbros and Pegmatites (Talabi

and Tijani, 2011).

Figure 1: Base map of the study area showing the VES points

III. Methodology and Instrumentation

The site investigation involved Vertical Electrical Sounding (VES) and direct boring (borehole) methods. The vertical electrical

sounding (VES) was carried out using Campus Omega Resistivity meter, four electrodes, measuring tape and hammers. The

Vertical Electrical Sounding (VES) technique involving Schlumberger array was adopted with maximum half current electrode

spacing (AB/2) of 200m. A total of five (5) VES stations were occupied at the logged borehole locations. The apparent resistivity

data obtained were plotted against half current spacing (AB/2) on a bi-log graph to determine the number of subsurface layers,

their resistivities as well as thicknesses using manual partial curve matching method with the aids of two layer model curves. The

initial model parameters resulting from the curve matching procedures were then fed into the computer for iteration processing

using WINRESIST software to obtain the final curves as well as the final model parameters (figure 2).

Figure 2: Electrical Resistivity Sounding Curves

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IV. Results and Discussion

Table 1 shows the results of the interpreted VES curves while figure 3 shows the geoelectric logs prepared from the results of the

vertical electrical sounding. The borehole litholgic logs are presented in figure 4.

4.1 Vertical Electrical Sounding

Two types of curve – HA and KA, and four major layers consisting of topsoil, lateritic clay, weathered basement and presumably

fresh bedrock were identified from the VES curves in study area. The resistivity of the topsoil ranges from 97.9 ohm-m to 258

ohms-m and its thickness varies from 0.5 - 1.1 m. Lateritic clay has resistivity values ranging from 85.3 ohm-m to 138.6 ohm-m

with thickness variation of 1.1 – 2.3 m. The resistivity of weathered basement ranges from 47.2 ohm-m to 385.5 ohm-m with

thickness between 4.4 – 21 m. The presumably fresh bedrock has resistivity values ranging from 1888.1 ohm-m to 14759.3 ohm-

m. These high resistivity values confirm the non-fractured and non-conductivity nature of the bedrock. The low values of

resistivity in the weathered layer are indicative of the presence of weathering agent which is likely to be water.

4.2 Borehole Lithologic Log

The borehole lithologic data of the Five VES points in the study area were obtained from the drilling company. The total number

of layers in the lithologic log agrees with the number of layers in the geoelectric log. The lithologic log shows that the thickness

of topsoil varies from 0.5 m to 1.1 m while the thicknesses of lateritic clay and weathered basement range from 1.4 m to 4.0 m

and 7.0 m to 25.5 m respectively. The drilling extended to different depths, ranging from 16.8 m to 66 m, in the fresh basement at

the VES points but the thickness of the fresh basement in the geoelectric log is infinity.

Table 1: Summary of VES Analysis

VES No

Layer

Resistivity

(Ωm)

Thickness (m)

Depth (m)

Resistivity

curve type

Lithology

258.0

139.4

153.1

5868.6

0.5

2.6

21.0

∞

0.5

3.1

24.1

∞

Topsoil

Lateritic clay

W/basement

Fresh basement

115.3

322.9

79.8

1888.1

0.7

2.3

10.7

∞

0.7

3.1

13.8

∞

Topsoil

Lateritic clay

W/basement

Fresh basement

137.8

421.2

47.2

4167.9

0.5

1.1

5.7

∞

0.5

1.6

7.3

∞

Topsoil

Lateritic clay

W/basement

Fresh basement

97.9

85.3

385.8

14759.3

1.0

2.9

4.4

∞

1.0

3.9

8.3

∞

Topsoil

Lateritic clay

W/basement

Fresh basement

218.4

138.6

155.5

1903.2

1.1

2.9

10.3

∞

1.1

3.9

14.3

∞

Topsoil

Lateritic clay

W/basement

Fresh basement

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Figure 3: Geoelectric logs

Figure 4: Borehole lithologic logs

4.3 Correlation and Regression Analyses of Geoelectric and Borehole lithologic logs

The correlation between the predicted depth to the basement derived from the VES study and the actual depth to basement from

the borehole logs (Table 2) has been computed using correlation and regression analyses.

Erricaker (1971) provides the expression for correlation coefficient as:

Γxy =

𝐶𝑜𝑣 (𝑥,𝑦)

𝛿𝑥𝛿𝑦

where  1   xy  1 (1)

Cov (x,y) is covariance of variables x and y; and x and y are standard deviation of x and y, respectively.

The regression analysis is defined by the following equations:

Y = a + bx (2)

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y = na + bx (3)

xy = ax + bx

(4)

Where 'a' and 'b' are constants, 'n' represents the number of items, and 'y' and 'x' denote variables, specifically, the actual depth to

the basement and the predicted depth to the basement, respectively, in this context (Table 3).

Table 2: Depths to bedrock from VES data and borehole data

VES No

Predicted Depth to Basement from Geoelectric

Log (m)

Actual Depth to Basement from Borehole

Lithologic Log (m)

24.1

28.9

13.8

16.9

7.3

9.0

8.3

12.5

14.3

22.1

Table 3: Analysis table for the depths to bedrock from VES data and borehole data

24.1

28.9

580.81

696.49

10.54

6.22

696.49

111.0916

38.6884

13.8

16.9

190.44

233.22

0.24

-4.08

233.22

0.0576

16.6464

7.3

9.0

53.29

65.7

-6.26

-10.58

65.7

39.1876

111.9364

8.3

12.5

68.89

103.75

-5.26

-9.58

103.75

27.6676

91.7764

14.3

22.1

204.49

316.03

0.74

-3.58

316.03

0.5476

12.8164

The graphical relationship between the predicted depth to basement from the geoelectric log and the actual depth to basement

from the borehole lithologic log in the study area was obtained using excel statistical tool (figure 5). A positive correlation

coefficient, Γxy, of 0.9 was obtained from the correlation coefficient analysis of the data and the regression analysis produced a

regression equation of y = 1.14x + 2.48.

Figure 5: Relationship between depths to bedrock from borehole and geoeletric logs

y = 1.1373x + 2.4808

R² = 0.931

0 5 10 15 20 25 30

Depth to bedrock determined from borehole

lithologic log (m)

Depth to bedrock determined from geoelectric log (m)

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V. Conclusion

Vertical electrical sounding (VES) technique has been proven to be a reliable technique in electrical resistivity method to give

information about the subsurface geology. The processes involved in generating geoelectric log is less expensive than drilling to

produce borehole lithologic log. The predicted depth to the basement as determined by the VES study has been compared with the

depth to basement from borehole logs within the study area, employing correlation and regression analyses. The correlation

coefficient value indicates a good level of correlation between the two set of data. Therefore, the obtained regression equation can

be used to generate borehole lithologic log from the geoelectric log within the study area. Using regression analysis, the

geoelectric log produced from the result of an electrical resistivity survey can be converted to a borehole lithologic log with a

high level of accuracy, under the assumption that there is no significant variation in the subsurface lithology within the study area.

Acknowledgements

The Authors of this study acknowledge with thanks Jadeko Geosciences Nigeria Limited for supplying information about the

boreholes.

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