Oluseun Adetola Sanuade

In Situ Determination of Thermal Resistivity of Soil: Case Study of Olorunsogo Power Plant, Southwestern Nigeria

This study measured in situ the thermal resistivity of soils at Olorunsogo Gas Turbine Power Station (335 MW Phase 1) which is located in Ogun State, Southwestern Nigeria. Ten pits, each of about 1.5 m below the ground surface, were established in and around the power plant in order to measure the thermal resistivity of soils in situ. A KD 2-Pro was used for the in situ measurement of thermal properties. Samples were also collected from the ten pits for laboratory determination of the physical parameters that influence thermal resistivity. The samples were subjected to grain size distribution analysis, compaction, specific gravity and porosity tests, moisture content determination, and XRD analysis. Also, thermal resistivity values were calculated by an algorithm using grain size distribution, dry density, and moisture content for comparison with the in situ values. The results show that thermal resistivity values range from 34.07 to 71.88°C-cm/W with an average of 56.43°C-cm/W which falls below the permissible value of 90°C-cm/W for geomaterials. Also, the physical parameters such as moisture content, porosity, degree of saturation, and dry density vary from 13.00 to 16.20%, 39.74 to 45.64%, 40.72 to 63.52%, and 1725.05 to 1930.00 Kg/m3, respectively. The temperature ranges from 28.92 to 35.39°C with an average of 32.11°C in the study area. The calculated thermal resistivity from an algorithm was found to vary from 48.43 to 81.22°C-cm/W with an average of 65.56°C-cm/W which is close to the thermal resistivity values measured in situ. Good correlation exists between the in situ thermal resistivity and calculated thermal resistivity with 𝑅=

Using artificial neural network to predict dry density of soil from thermal conductivity

Abstract Artificial neural network (ANN) was used to predict the dry density of soil from its thermal conductivity. The study area is a farmland located in Abeokuta, Ogun State, Southwestern Nigeria. Thirty points were sampled in a grid pattern, and the thermal conductivities were measured using KD-2 Pro thermal analyser. Samples were collected from 20 sample points to determine the dry density in the laboratory. MATLAB was used to perform the ANN analysis in order to predict the dry density of soil. The ANN was able to predict dry density with a root-mean-square error (RMSE) of 0.50 and a correlation coefficient (R2) of 0.80. The validation of our model between the actual and predicted dry densities shows R2 to be 0.99. This fit shows that the model can be applied to predict the dry density of soil in study areas where the thermal conductivities are known.

Preliminary geotechnical characterization of a site in southwest Nigeria using integrated electrical and seismic methods

Geophysical investigation using Vertical Electrical Sounding (VES), Electrical Resistivity Tomography (ERT) and Seismic Refraction at a proposed conference center site along Ajibode-Labani road, Ibadan, southwestern Nigeria has been carried out. The investigation aims at characterizing and delineating the subsurface strata to understand the weathered profile at the site. Understanding the weathered profile is essential in determining the suitability of the site for engineering construction of the future conference center. A total of 25 VES and 10 ERT profiles were acquired in a systematic grid pattern using both Schlumberger andWenner configurations with Allied omega terrameter. TheVES data were processed and analyzed using WinResist and the ERT data were inverted using RES2DINV. The data were combined to form a 3-D data set of the site and RES3DINV was used to produce the depth slices. Seismic refraction data were also acquired with an ABEM seismograph and processed using SeisImager and Fajseis software. Seismic data were used in understanding the velocity distribution and thickness. The results of VES, ERT and seismic refraction show good correlation. Four sub-surface layers were delineated: top layer of reworked sand, clayey sand/ lateritic hard pan, clay/ sandy clay and fracture/ fresh basement. The 3-D model permits a pictorial view of the sub-surface in relation to materials that overlie the basement. The thickness of unconsolidated materials to bedrock varies from 2.7 m to 12.2 m which revealed inhomogeneity in weathering under the shallow sub-surface. It is found that the integrated geophysical tool is well suited to characterize and delineate sub-surface structure (weathered profile) for engineering site characterization.

The use of multivariate statistical analysis in the assessment of groundwater hydrochemistry in some parts of southwestern Nigeria

Multivariate statistical methods including factor analysis (FA), cluster analysis (CA), and correlation analysis have been used to evaluate the spatial variations and the interpretation of a complex water quality data set of some parts of Oyo State in southwestern Nigeria. Thirty water samples were collected from different stations, and 16 parameters were determined. Correlation analysis shows that the relationship between the parameters with high character of ion was higher than that of the parameters with low character of ion and that the variation in relationship shows the complexity in groundwater quality and the effect of the interactions between rock and water. Regression analysis was used for the prediction of values of one variable using the knowledge of other variables, for which more data are available. FA shows five distinct factors, which explained 84.3% of the total variance in water quality data set. The five factors are anthropogenic, ion exchange, weathering/leaching, anthropogenic, and nitrogen, which explained 28, 23, 14.2, 10.0, and 6.9% of the total variance, respectively. Hierarchical cluster analysis grouped the parameters into three major clusters. This study shows the uses of multivariate statistical methods for the interpretation of complex data sets in the analysis of spatial variations in water quality. This would therefore enhance planning for future studies.

A Resistivity Survey of Phosphate Nodules in Oshoshun, Southwestern Nigeria

Abstract This geophysical study was carried out to determine the occurrence of phosphate nodules in the Oshoshun Formation of the Dahomey Basin, Southwestern Nigeria. The electrical resistivity method, comprising 1D vertical electrical sounding (VES; using Schlumberger array) and 2D geoelectrical imaging (using Wenner array), was used to determine the nature and depth of occurrence of the phosphate nodules. Six profile lines were established within the study area, and inverted sections were generated from the apparent resistivity data using DIPRO inversion algorithm. Five VES points were also acquired in the study area, and Win- Resist programme was used to process and interpret the field resistivity data. Four pits were dug along the profiles to verify the interpreted results. The results obtained by both techniques reveal similar geoelectric units: the top soil, clay, clayey sand and clay at different depths. These layers host pockets of phosphate nodules (78-≥651 Ωm) with varying thicknesses. The strong correlation between the lithology profiles obtained from the pits and the interpreted results of the inverted apparent resistivity sections demonstrates the efficacy of the electrical resistivity method in characterising phosphate occurrence within the formation.

Geostatistical modeling of porosity data in ‘oba’ field, onshore Niger Delta

Abstract A geostatistical approach was used to model porosity of OBA field in onshore area of Niger Delta using simulation technique. The objective is to understand the spatial distribution of porosity and characterize the degree of heterogeneity of underlying formation. Porosity data from twenty-two wells were loaded into SGeMS software. Univariate statistical analysis, experimental semivariogram and Sequential Gaussian Simulation (SGS) were applied on the data. The data was close to normal approximation of Gaussian based of the results of univariate statistics. However, to construct and model horizontal and vertical semivariograms, the data was log-normalized to reduce the coefficient of variation and to get good fit of the model. Parametric semivariogram model shows the range of 72–6480 m, nugget effect of 0.006 and sills of 0.0095, 0.0099 and 0.0111. Six realizations were generated using SGS algorithm and the results suggest that any one of the realizations can independently represents the true picture of the subsurface geology within the study area. Ranking of realizations shows realization 6 as the best and realization 2 as the lowest. This model could be used as an initial condition for simulation of flow.

The use of geological-based geophysical surveys for groundwater distribution in crystalline basement terrain, SW Nigeria

This research involves the subsurface geological characterization for groundwater potential assessment within the campus of the Polytechnic of Ibadan, southwestern Nigeria. The study is directed towards groundwater resources exploration, development and management in the campus. Five 2D resistivity imaging traverses were conducted using Wenner array in addition to five VES surveys using Schlumberger array that provide layering information and geoelectrical parameters. Three geologic layers delineated from the 2D resistivity inversion models include predominantly clayey sand/ sandy clay top soil (overburden), partly weathered or fractured basement and fresh basement. Their inverse model resistivity values ranges 6.68 – 98.6m , 68.0 – 929 m and  2252 m with bottom depths ranges 3.8 – 6.4 m and 6.4 – 10 m respectively. 1D model inversion from VES results also delineate three lithologies classifying both topsoil and some part of the partly weathered basement as overburden with resistivity and thickness range 483 – 1746.9 m , 1.1 – 1.8 m; partly weathered or fractured basement 60.3 – 93.5 m , 8.4 -12.9 m and fresh basement 984.6 – 2078.9 m . The saturated portion of the partly weathered or fractured basement at depth will favour groundwater exploration and development in this area, while the relatively shallow overburden thickness would serve as the protective layer and recharge for the fractures.

Data on the thermal properties of soil and its moisture content

The dataset contains thermal properties of soil such as thermal conductivity, thermal diffusivity, temperature and specific heat capacity in an agricultural farm within the University of Ibadan, Ibadan, Nigeria. The data were acquired in forty (40) sampling points using thermal analyzer called KD-2 Pro. Soil samples taken at these sampling points were analyzed in the laboratory for their moisture content following the standard reference of American Association of State Highway and Transport Officials (AASHTO) T265. The data were acquired within the first and second weeks in the month of April, 2012. Statistical analyses were performed on the data set to understand the data. The data is made available publicly because thermal properties of soils have significant role in understanding the water retention capacity of soil and could be helpful for proper irrigation water management.

Predicting moisture content of soil from thermal properties using artificial neural network

Monitoring of soil moisture contents is an important practice for irrigation water management. The benefit of periodic soil water content data includes improved irrigation scheduling in order to optimize water usage for improved crop productivity. However, the in situ equipment for measuring soil water contents have high maintenance and operation cost and are highly affected by neighboring soil conditions, and some have overwhelming calibration and data interpretation, whereas the common standard laboratory procedure requires much effort and can be time-consuming for large dataset. The objective of this study is to evaluate the applicability of artificial neural network (ANN) to predict moisture content of soil using available or measured thermal properties (thermal conductivity, thermal diffusivity, specific heat, and temperature) of soil. We used both multilayered perception (MLP) and radial basis function (RBF) types of ANN. The study area is a farmland situated within the premises of the University of Ibadan campus. Thermal properties were measured with KD2 Pro at 42 points along seven transects. Soil samples were also collected at these points to determine their moisture contents in the laboratory. ANN analysis carried out effectively predicted the soil moisture content with very low root-mean-square error (RMSE) and high correlation coefficient (R) of approximately 0.9 for the two methods evaluated. The overall results suggest that ANN can be incorporated to predict the moisture content of soil in this area where thermal properties are known.

Hydrocarbon reservoir characterization of “AY” field, deep-water Niger Delta using 3D seismic and well logs

Three-dimensional seismic and well log data from nine wells were used for the characterization of “AY” field in the deep-water, Niger Delta. Result shows that the field has a complex structural arrangement consisting of series of northeast-southwest-trending and northwest-dipping synthetic faults. Petrophysical evaluation of the available well logs helped in identifying 11 hydrocarbon-bearing sands noted as A1000, A1100, A1200, A2000, B2000, B2100, C3000, C3100, D4000, D4100, and E5000. Reservoirs A1000, A1100, A1200, A2000, B2000, C3000, and D4000 are gas-bearing sands while reservoirs B2100, C3100, D4100, and E5000 are oil bearing. The average effective porosity of these reservoirs ranges from 0.168 to 0.292; water saturation is estimated to be between 0.177 and 0.59 and net-to-gross (NTG) ratio from 0.081 to 0.734. Considering the uncertainty in the input petrophysical parameters as well as structural uncertainty particularly in fluid contact, the total hydrocarbon reserves in the field were estimated to vary between 266.942 and 334.457 Bscf and 132.612 and 150.036 MMbbl for gas and oil volumes, respectively.