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

Abstract

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.

3D Reverse-Time Migration Imaging for Multiple Cross-Hole Research and Multiple Sensor Settings of Cross-Hole Seismic Exploration

“…The reverse-time migration (RTM) method based on the wave equation can adapt to strong lateral variations in the velocity field and is considered one of the most accurate imaging methods [29][30][31]. In recent years, the 3D RTM method has been applied in the field with promising cross-hole results [32][33][34]. Compared with traditional ray-based cross-well methods, RTM includes more wavefield information, allowing seismic wave energy to be returned to its true location in space, obtaining more accurate geological structures and providing higher-quality images [35,36].…”

An Application of 3D Cross-Well Elastic Reverse Time Migration Imaging Based on the Multi-Wave and Multi-Component Technique in Coastal Engineering Exploration

“…Cross-well seismic can be applied to various aspects of oil and gas exploration, including detailed imaging of structures and precise characterization of reservoirs [10][11][12]. Furthermore, cross-well seismic has also been rapidly developed in the field of engineering [13,14] and has been applied in diverse areas, such as geological engineering [15][16][17], hydrogeological surveys [18][19][20], and quality inspections in civil engineering projects [21][22][23]. Currently, most cross-well seismic studies focus on two-dimensional (2D) tomographic imaging between adjacent wells [24,25].…”

Hybrid Discrete Fracture Network Inversion of Hydraulic Tomography Data From a Fractured‐Porous Field Site

“…It is assumed that the heterogeneities in hydraulic parameters are attributed exclusively to matrix flow, meaning that individual (discrete) fractures cannot be inferred. The continuum model is well established for the characterization of porous and fractured sites (e.g., Berg & Illman, 2011;Cardiff et al, 2013Cardiff et al, , 2019Fischer et al, 2017;Illman et al, 2009;Liu et al, 2022;Luo et al, 2023;Ren et al, 2021;Tiedeman & Barrash, 2020;Zha et al, 2015;Zhao et al, 2019Zhao et al, , 2023 and several inversion methods have been developed based on this concept. While geostatistical approaches can generate tomograms for both hydraulic conductivity (K) and specific storage (S S ) and often produce the best results for drawdown predictions, continuum-based travel time inversion only yields diffusivity (D) tomograms, but allows for the accurate characterization of high-D zones with less hydraulic data being required for the inversion (H. Qiu et al, 2023).…”