Geostatistical analysis of high-resolution hydraulic conductivity estimates from the hydraulic profiling tool and integration with hydraulic tomography at a highly heterogeneous field site

Evaluation of Hydraulic Conductivity Estimates from Various Approaches with Groundwater Flow Models

“…While the accurate prediction of drawdowns from pumping tests is promising, further studies are needed to see whether these K distributions are useful for contaminant transport predictions. Our research suggests that inverse modeling is a necessary step in building more robust groundwater flow models echoing suggestions by Poeter and Hill (1997) and Carrera et al (2005). HT additionally fuses information from multiple pumping tests and can integrate other data such as from geological investigations (e.g., Zhao and Illman 2018), geophysical surveys (e.g., Soueid Ahmed et al 2014), flowmeter surveys (Li et al 2008; Aliouache et al 2021; Luo et al 2023), tracer tests (e.g., Yeh and Zhu 2007; Illman et al 2010; Doro et al 2014), and high‐resolution pressure (Zhao and Illman 2022a) as well as K estimates from HPT surveys (Zhao et al 2023) that further improves parameter estimates. However, HT should not be considered a panacea technology as the parameter estimates are highly dependent on model conceptualization, accuracy of data fed into models including forcing functions (i.e., initial and boundary conditions, source/sink terms) applied to models.…”

“…However, quantitative synthesis of these images to yield accurate K and S s values requires advanced inverse modeling techniques. Over the last two decades, HT has been tested through a number of synthetic (e.g., Yeh and Liu 2000; Bohling et al 2002; Zhu and Yeh 2005; Xiang et al 2009; Hu et al 2011), laboratory (e.g., Liu et al 2007; Berg and Illman 2011a; Zhao et al 2015, 2022; Luo et al 2017; Jiang et al 2021), and field studies (Bohling et al 2007; Straface et al 2007; Illman et al 2009; Castagna et al 2011; Huang et al 2011; Berg and Illman 2011b; Brauchler et al 2013; Cardiff et al 2013; Zha et al 2016, 2019; Fischer et al 2018; Zhao and Illman 2018, 2022a; Tiedeman and Barrash 2020; Luo et al 2022; Ning et al 2023; Zhao et al 2023). HT data from pumping or injection tests can be inverted sequentially or simultaneously, while treating the medium to be homogeneous, consisting of geology‐based zonations, or highly parameterized (Illman et al 2015).…”

Hydraulic Tomography Estimates Improved by Zonal Information From the Clustering of Geophysical Survey Data

“…A volumetric algorithm called the fast radial basis function (Carr et al., 2001) is applied in the Leapfrog Geo software to produce first‐pass surfaces and continuous smooth models utilizing a global neighborhood searching algorithm from categorical data along boreholes; and (c) geophysical measurements are converted to K values if petrophysical relationships exist (e.g., NMR). Alternatively, if local K measurements/estimates from grain size analyses, borehole hydraulic tests (e.g., permeameter, slug, and/or single‐holes tests or from the Hydraulic Profiling Tool [McCall & Christy, 2020; Zhao et al., 2023]) are available, the geometric mean of collected K measurements in each zone can be calculated and utilized as an initial realization of the effective mean value in this zone; (d) following the generation of zone geometry, zonal K values are optimized using head response data from pumping tests by coupling a model‐independent parameter estimation code PEST (Doherty, 2015) with the groundwater flow simulator in VSAFT3 (variably saturated flow and transport 3‐D model, Yeh et al., 1993). If no local K measurements are available, an unconditional effective mean of K for the entire simulation domain is applied.…”

“…A more detailed description of the SimSLE algorithm can be found in Xiang et al (2009). Christy, 2020; Zhao et al, 2023]) are available, the geometric mean of collected K measurements in each zone can be calculated and utilized as an initial realization of the effective mean value in this zone; (d) following the generation of zone geometry, zonal K values are optimized using head response data from pumping tests by coupling a model-independent parameter estimation code PEST (Doherty, 2015) with the groundwater flow simulator in VSAFT3 (variably saturated flow and transport 3-D model, Yeh et al, 1993). If no local K measurements are available, an unconditional effective mean of K for the entire simulation domain is applied.…”

Pumping‐induced Well Hydraulics and Groundwater Budget in a Leaky Aquifer System with Vertical Heterogeneity in Aquitard Hydraulic Properties

“…To comprehensively consider the relatively large compressibility (Leake, 1990;Li et al, 2020), comparative thickness (Zhou et al, 2013) and complex sedimentary processes, aquitards also have such significant layered heterogeneity characteristics at the field scale (Liu and Ball, 1998;Xu et al, 2009;Ding et al, 2022) and depth-decaying heterogeneity at the regional scale (Keller et al, 1989;Kamp, 2001;Ferris et al, 2020). Recently, there has been a growing interest in characterizing aquitard heterogeneity (e.g., Zhao et al, 2023;Zhuang et al, 2023). The field-scale layered heterogeneity has been found in both aquitard K and S s (Zhuang et al, 2017;Chapman et al, 2018;Zhuang et al, 2019).…”