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  • Author: Joel Olayide Amosun x
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Preliminary geophysical investigation for road construction using integrated methods

Abstract

Integrated geophysical methods have been used to investigate the competency of the subsoil. The geophysical surveys conducted involve very low-frequency electromagnetic (VLF-EM) and electrical resistivity (ER) methods (dipole-dipole). ABEM Wadi and Ohmega resistivity meter were used to acquire VLF-EM and ER data, respectively, along two traverses. Station interval of 5 m was used for the VLF-EM survey, while inter-electrode spacing for dipole–dipole was 10 m; the inter-dipole expansion factor (n) ranged from 1 to 5. KHFFILT software was used to generate VLF-EM profiles and pseudosection, while DIPRO software was used for ER. Results from the ER method revealed the pattern of resistivity variations within the study area. The low resistivity values (11–25 Ohm-m) observed at the southern part of the study area could be attributed to changes in clay contents and degree of weathering in the subsurface. The results from the VLF-EM investigation revealed the presence of near-surface linear geologic structures of varying lengths, depths and attitudes, which suggest probable conductive zones that are inimical to the foundation of the road subgrade.

Open access
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.

Open access
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.

Open access