Estimating Hydraulic Conductivity Fields in Composite Fan Delta Using Vertical Electrical Sounding

Abstract

Hydraulic conductivity (K) is crucial for groundwater studies and is conventionally obtained through pumping tests, which are costly due to well installation, resulting in a limited amount of data. Recent studies have addressed this limitation by estimating K through the integration of a pumping-test K and electrical resistivity measurements. While this approach is suitable for local areas, it cannot readily be applied to determine K fields for the composite fan delta. This study proposes and demonstrates an advanced method to estimate the K's spatial distribution of a composite fan delta. The proposed method included data classification, linear regression, and kriging interpolation. Data classification was conducted using a physical-based zonation method. The K and formation factor (F) data pairs were classified into several groups. Linear regression was used to develop K-F mapping for each group. The regression results showed a good correlation between K and F in each group. These K-F mappings were verified by additional pumping tests. These results indicate that estimation errors were between 7 m/day and 58 m/day, and the correlation coefficient of each data group was greater than 0.8. Based on these regression equations and ordinary kriging method, the detailed K spatial distribution of the study area was derived. According to these results, the proposed method is efficient and economical to delineate K's spatial features for regional groundwater systems and can benefit groundwater-related studies.

Investigating Formation Factor–Hydraulic Conductivity Relations in Complex Geologic Environments: A Case Study in Taiwan

“…As a result, the analysis shows a negative correlation where higher COND values correspond to smaller K values. Although COND may correlate with K, COND is not directly used to estimate K. Instead, the reciprocal of COND was transformed into electrical resistivity, which is used as the resistivity of the water saturating the pores (R w ), as shown in Equation (1). It is then combined with SHN to calculate F.…”

“…Hydraulic conductivity plays an essential role in controlling the distribution, flow path, and storage of groundwater in the formation, especially in complex geological environments where this parameter is highly heterogeneous and, as generally recognized, may vary with spatial locations [1][2][3][4]. Therefore, if detailed and continuous hydraulic conductivity along boreholes (hydraulic conductivity profiles) can be obtained during the site investigation [5], it will be constructive to clarify the hydrogeological complexity of rock formations [6].…”

Bayesian Integration Using Resistivity and Lithology for Improving Estimation of Hydraulic Conductivity

“…In contrast, categorical data (e.g., lithology description) are much less frequently used as secondary data in data integration although they are also abundant and can represent different materials in composite approaches (Y. C. Chen et al., 2018; Fogg et al., 1998; Straface et al., 2011). The challenge of using category data in data integration is similar to or even more formidable than that of using geophysical data because of the ambiguous relation and scale disparity with the primary data.…”

“…Regional-scale characterization of K using data integration is much less common in the literature. Y. C. Chen et al (2018) attempted to determine the K field of a large-scale composite fan delta using a threestage approach. Zonation was first applied to the alluvial fan based on location, geometry, and clay content of lithofacies.…”

Flow characterization and modelling of a South Benin artesian aquifer system : Effects of hydraulic and storage properties and recharge on simulated heads across the artesian zone

“…: Hao et al, 2012) and geophysical techniques (e.g. : Magnetic Resonance and Vertical Electrical soundings) could be used to derive hydraulic and storage properties of the subsurface layers (Gunther and Muller-Petke, 2012;Chen et al, 2018;Perdomo et al, 2018). However, it should be kept in mind that properties derived from both laboratory and non-invasive methods do not necessary reflect field conditions (Picornell et al, 1992;Bagarello, and Provenzano, 1996).…”