Innovative hydrogeophysical approach for detecting the spatial distribution of hydraulic conductivity in Bahri city, Sudan: A comparative study of Csókás and Heigold methods

Abstract

Hydraulic conductivity is one of the crucial parameters used to identify the potentiality and productivity of groundwater aquifers. This research employs an integrated approach using geophysical well logging, exploratory factor analysis and surface electrical resistivity methods to detect the vertical and horizontal variation of hydraulic conductivity in Bahri city, Sudan. Based on the geophysical well logs of Spontaneous potential (SP), natural gamma ray (GR), and electrical resistivity (RS), Csókás method is used to determine the continuous variation of hydraulic conductivity along the aquifer. Csókás method is an experimentally modified version of the Kozeny–Carman equation and is based on the formation factor of the groundwater aquifer and the effective grain size. This approach is performed in three groundwater boreholes, and the obtained hydraulic conductivities showed a close agreement with that of the pumping test analysis. Furthermore, the hydraulic conductivity is measured using multivariate statistical factor analysis. This statistical approach relies on the correlation between the extracted factors and petrophysical and hydrogeological parameters. In this research, a strong negative linear correlation between the first factor and hydraulic conductivity is indicated. Consequently, a site-specific equation is suggested for continuous estimation of hydraulic conductivity along the aquifer. In the last stage, the results obtained from the Csókás method are interpolated with vertical electrical sounding (VES) measurements using to detect the horizontal variation of hydraulic conductivity throughout the studied area. This was achieved by combining the hydraulic conductivities of geophysical well logging and vertical electrical soundings to obtain a consistent estimation. As a result, the variation of hydraulic conductivity is obtained, and the average was 1.9 m/day which shows a close agreement with the average of the previous investigations (1.5 m/day). This approach is highly recommended since it can enhance data coverage, cutting down the expense of hydrogeological investigations and lowering the uncertainty of the hydrogeological models.

Management of Agricultural Groundwater in Sudan: The Use of Artificial Intelligence Algorithms in Khartoum State

“…This study explores the suitability of groundwater for irrigation purposes in Khartoum state, Sudan (Figure 1). The area is characterized by a hot climate in summer, cold and dry in winter, and associated with an annual average precipitation of 115.7 mm/year in the fall season [10]. The main geomorphological features are the Nile River which bounds the study area from the west.…”

“…The geographical location and the main geological units in the study area modified after [10] Figure 2 The water level map shows the main groundwater flow direction in the study area…”

Assessing heterogeneous groundwater systems: Geostatistical interpretation of well logging data for estimating essential hydrogeological parameters

“…In this context, Csókás 26 introduced an improved methodology for estimating hydraulic conductivity in loosely consolidated hydrogeological units, exclusively relying on well-logging data. The successful application of this method necessitates the interpretation of geophysical logs sensitive to lithology and water saturation 27 . The main advantage of well logging-based methods is their ability to give continuous profile estimation for petrophysical and hydrogeological parameters crucial to simulating and understanding the hydrodynamic conditions of the heterogeneous groundwater systems 28 , 29 .…”