Godspower Onyekachukwu Ekwueme
Department of Industrial and Production Engineering, Faculty of Engineering, Nnamdi Azikiwe University Awka, Anambra State, Nigeria.

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INTEGRATED GEOELECTRIC AND HYDROGEOLOGICAL MODELING FOR AQUIFER CHARACTERIZATION AND OPTIMAL BOREHOLE SITING IN OBUKPA, ENUGU STATE, NIGERIA Godspower Onyekachukwu Ekwueme
Frontier Energy System and Power Engineering Vol 8, No 1 (2026): January
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v8i1p1-12

Abstract

Water is an essential resource for domestic, agricultural and industrial activities, especially in areas where surface water supply is scarce. But growing population, climate change and inadequate subsurface information has resulted in borehole failures and inefficient use of groundwater resources in places like Obukpa watershed, Enugu State. The research seeks to solve the issues of uncertain borehole selection due to insufficient knowledge of the hydrogeology and singular methods of groundwater exploration. This research is warranted by the need to increase the success rate of groundwater exploration through an integrated geoelectric and hydrogeological modelling technique that will minimise uncertainties and optimise sustainable water resource development. The research objectives were to map the lithology, aquifers, estimate hydrogeological parameters, classify aquifer productivity, integrate a model, and establish borehole drilling depths. Primary data sources were Vertical Electrical Sounding (VES) using the Schlumberger array, while secondary data included hydrogeological information. Methods applied were log₁₀ resistivity transformation, regression analysis, rule-based classification, productivity index modelling and model diagnostics. This identified a layered subsurface with resistivity values between 538.74-5387 Ω·m, and a highly productive saturated sand unit at 203.43-253.43 m with resistivity of 1315.8 Ω·m (log₁₀ = 3.119), and productivity index of 1.0995, which corresponds to an excellent aquifer. The regression model explained 92.09% variability (R²) of the data but was not significant at p = 0.1163. The best drilling depth was found to be between 198-213 m (average 205.5 m). The research concludes that integrated modelling enhances aquifer identification and borehole productivity. It calls for mandatory pre-drilling geophysical studies and groundwater databases to manage the resource.