Large-scale three-dimensional hydraulic tomography analyses of long-term municipal wellfield operations

Section: Hou Et Almentioning

confidence: 99%

A Short‐Term Pumping Strategy for Hydraulic Tomography Based on the Successive Linear Estimator

Hou

Yeh

et al. 2023

“…However, questions remain regarding the reliability of small-scale K estimates and the spatial interpolation method in representing the connectivity of hydraulic parameters between and beyond boreholes (Illman, 2014). both porous and fractured media has been shown through numerical (e.g., Luo et al, 2020;Yeh & Liu, 2000;, laboratory (e.g., Berg & Illman, 2011a;Illman et al, 2007Illman et al, , 2010Illman et al, , 2015Liu et al, 2002;Luo et al, 2017;Sharmeen et al, 2012;Zhao et al, 2015Zhao et al, , 2021Zhou et al, 2016) and field (e.g., Berg & Illman, 2011b;Bohling et al, 2007;Cardiff et al, 2012Cardiff et al, , 2013Cardiff et al, , 2020Castagna et al, 2011;Illman et al, 2009;Luo et al, 2022;Zha et al, 2016;, 2018 studies. However, the estimated K distribution may be spatially smooth due to insufficient spatial head data or lose its vertical resolution due to the use of depth-averaged head data.…”

Section: Introductionmentioning

confidence: 99%

“…HT uses an inverse algorithm (e.g., Bohling et al., 2002; Brauchler et al., 2003; Cardiff et al., 2009; Kitanidis & Lee, 2014; Yeh & Liu, 2000; Zhu & Yeh, 2005) to interpret head responses at monitoring wells during a series of pumping/injection tests at different locations. The robust performance of HT for mapping heterogeneity details of hydraulic parameters for both porous and fractured media has been shown through numerical (e.g., Luo et al., 2020; Yeh & Liu, 2000; Zhu & Yeh, 2005), laboratory (e.g., Berg & Illman, 2011a; Illman et al., 2007, 2010, 2015; Liu et al., 2002; Luo et al., 2017; Sharmeen et al., 2012; Zhao et al., 2015, 2021; Zhou et al., 2016) and field (e.g., Berg & Illman, 2011b; Bohling et al., 2007; Cardiff et al., 2012, 2013, 2020; Castagna et al., 2011; Illman et al., 2009; Luo et al., 2022; Zha et al., 2016; Zhao & Illman, 2017, 2018) studies. However, the estimated K distribution may be spatially smooth due to insufficient spatial head data or lose its vertical resolution due to the use of depth‐averaged head data.…”

Section: Introductionmentioning

confidence: 99%

Three‐Dimensional Steady‐State Hydraulic Tomography Analysis With Integration of Cross‐Hole Flowmeter Data at a Highly Heterogeneous Site

Luo

Zhao

Illman

et al. 2023

Self Cite

Hydraulic tomography (HT) has been shown to be a robust approach for the high‐resolution characterization of subsurface heterogeneity. However, HT can yield smooth estimates of hydraulic parameters when pumping tests and drawdown measurements are sparse, thus limiting the utility of characterization results in predicting groundwater flow and solute transport. To overcome this issue, this study integrates cross‐hole flowmeter measurements with HT analysis of steady‐state pumping/injection test data for the three‐dimensional (3‐D) characterization of hydraulic conductivity (K) at a highly heterogeneous glaciofluvial deposit site, which has not been previously attempted. Geostatistical inverse analyses of cross‐hole flowmeter data are conducted to yield preliminary estimates of K distribution, which are then utilized as initial K fields for steady‐state HT analysis of head data. Four cases combining three data types (geological information, cross‐hole flowmeter measurements, and steady‐state head data) for inverse modeling are performed. Model calibration and validation results from all cases are compared qualitatively and quantitatively to evaluate their performances. Results from this study show that (a) geostatistical inverse analysis of cross‐hole flowmeter data is capable in revealing vertical distributions of K at well locations and major high/low K zones between wells, (b) cross‐hole flowmeter data carry non‐redundant information of K heterogeneity compared to geological information and steady‐state head data, and (c) integration of flowmeter data improves characterization results in terms of revealing K heterogeneity details and predicting independent hydraulic test data. Therefore, this study demonstrates the usefulness of cross‐hole flowmeter data in augmenting HT surveys for improved K characterization in 3‐D.

“…Liu, Yeh, Wang, Hao, et al, 2020;F. Liu, Yeh, Wang, Song, et al, 2020;Luo et al, 2022;Xia et al, 2023;Yeh et al, 2009).…”

Section: Introductionunclassified

“…To interpret the heterogeneity pattern of aquifer‐aquitard systems, geostatistical and stochastic inverse methods were developed (Gutjahr & Wilson, 1989; Harvey & Gorelick, 1995; Hoeksema & Kitanidis, 1984, 1989; Kitanidis, 1995; Kitanidis & Vomvoris, 1983; Rubin & Dagan, 1987; Yeh et al., 1995, 1996), in which geostatistical parameters of aquifer systems were introduced into the inversion, and the most likely spatial distribution of hydraulic properties and their uncertainty are estimated for accurate prediction of drawdown responses. To date, HT has been developed as a mature technique to more accurately map spatial heterogeneity of hydraulic properties through synthetic studies (Cardiff & Barrash, 2011; Gottlieb & Dietrich, 1995; Luo et al., 2020; Tso et al., 2015; Yeh & Liu, 2000; Zhu & Yeh, 2005, 2006), laboratory experiments (Berg & Illman, 2011a; Illman et al., 2012, 2008, 2007, 2010; S. Liu et al., 2002; X. Liu et al., 2007; Luo et al., 2017; Zhao & Illman, 2021), field studies (Berg & Illman, 2011b, 2013, 2015; Bohling et al., 2007; Brauchler et al., 2013; Cardiff et al., 2013; Huang et al., 2011; Illman et al., 2009; Klepikova et al., 2020; Tiedeman & Barrash, 2020; Zha et al., 2016; Zhao & Illman, 2017, 2018, 2022) and basin‐scale aquifer characterization (Daranond et al., 2020; F. Liu, Yeh, Wang, Hao, et al., 2020; F. Liu, Yeh, Wang, Song, et al., 2020; Luo et al., 2022; Xia et al., 2023; Yeh et al., 2009).…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Illman

2023

Self Cite

Hydraulic Tomography (HT) has been demonstrated as a robust approach to characterize subsurface heterogeneity through the inverse modeling of multiple pumping data. However, smooth or even erroneous tomograms can result when insufficient observations are involved in the inversion. In this study, the feasibility of integrating geophysical surveys into HT analysis is investigated. First, k‐means clustering is utilized to extract zonal information from borehole geophysical logs, and a new type of spatial constraints containing geological knowledge is proposed to obtain improved hydrostratigraphic boundaries along boreholes. Next, zonation models are constructed by applying clustering‐based zone geometry and populating zonal estimates of hydraulic conductivity (K) from analyzing pumping data. Afterwards, zonation models are treated as the initial guess of spatial variability in the geostatistical inversion of HT analysis. Additionally, local K measurements can be utilized to further improve HT estimates. Comparative cases of HT analyses are designed for a numerical sandbox experiment to highlight the HT performance integrated with geophysical surveys, in which the geostatistical inversion is initialized with: (1) a homogeneous K field; (2) zonation models built by the clustering of disparate geophysical surveys with/without spatial constraints; and (3) zonation improved by incorporating local K measurements. Based on ln K field comparisons and validation through predictions of drawdowns and tracer plume migration from independent tests not used in the calibration effort, we find that integration of geophysical surveys into HT analysis by clustering with spatial constraints is demonstrated as an effective approach, and local K measurements can further improve HT estimates.This article is protected by copyright. All rights reserved.