Real-time monitoring of in situ chemical oxidation (ISCO) of dissolved TCE by integrating electrical resistivity tomography and reactive transport modeling.

Successful in situ chemical oxidation (ISCO) applications require real-time monitoring to assess the oxidant delivery and treatment effectiveness, and to support rapid and cost-effective decision making. Existing monitoring methods often suffer from poor spatial coverage given a limited number of boreholes in most field conditions. The ionic nature of oxidants (e.g., permanganate) makes time-lapse electrical resistivity tomography (ERT) a potential monitoring tool for ISCO. However, time-lapse ERT is usually limited to qualitative analysis because it cannot distinguish between the electrical responses of the ionic oxidant and the ionic products from contaminant oxidation. This study proposed a real-time quantitative monitoring approach for ISCO by integrating time-lapse ERT and physics-based reactive transport models (RTM). Moving past common practice, where an electrical-conductivity anomaly in an ERT survey would be roughly linked to concentrations of anything ionic, we used PHT3D as our RTM to distinguish the contributions from the ionic oxidant and the ionic products and to quantify the spatio-temporal evolution of all chemical components. The proposed approach was evaluated through laboratory column experiments for trichloroethene (TCE) remediation. This ISCO experiment was monitored by both time-lapse ERT and downstream sampling. We found that changes in inverted bulk electrical conductivity, unsurprisingly, did not correlate well with the observed permanganate concentrations due to the ionic products. By integrating time-lapse ERT and RTM, the distribution of all chemical components was satisfactorily characterized and quantified. Measured concentration data from limited locations and the non-intrusive ERT data were found to be complementary for ISCO monitoring. The inverted bulk conductivity data were effective in capturing the spatial distribution of ionic species, while the concentration data provided information regarding dissolved TCE. Through incorporating multi-source data, the error of quantifying ISCO efficiency was kept at most 5 %, compared to errors that can reach up to 68 % when relying solely on concentration data.

Characterization of DNAPL source zones in clay-sand media via joint inversion of DC resistivity, induced polarization and borehole data.

Toxic organic contaminants in groundwater are pervasive at many industrial sites worldwide. These contaminants, such as chlorinated solvents, often appear as dense non-aqueous phase liquids (DNAPLs). To design efficient remediation strategies, detailed characterization of DNAPL Source Zone Architecture (SZA) is required. Since invasive borehole-based investigations suffer from limited spatial coverage, a non-intrusive geophysical method, direct current (DC) resistivity, has been applied to image the DNAPL distribution; however, in clay-sand environments, the ability of DC resistivity for DNAPLs imaging is limited since it cannot separate between DNAPLs and surrounding clay-sand soils. Moreover, the simplified parameterization of conventional inversion approaches cannot preserve physically realistic patterns of SZAs, and tends to smooth out any sharp spatial variations. In this paper, the induced polarization (IP) technique is combined with DC resistivity (DCIP) to provide plausible DNAPL characterization in clay-sand environments. Using petrophysical models, the DCIP data is utilized to provide tomograms of the DNAPL saturation (SN) and hydraulic conductivity (K). The DCIP-estimated K/SN tomograms are then integrated with borehole measurements in a deep learning-based joint inversion framework to accurately parameterize the highly irregular SZA and provide a refined DNAPL image. To evaluate the performance of the proposed approach, we conducted numerical experiments in a heterogeneous clay-sand aquifer with a complex SZA. Results demonstrate the standalone DC resistivity method fails to infer the DNAPL in complex clay-sand environments. In contrast, the combined DCIP technique provides the necessary information to reconstruct the large-scale features of K/SN fields, while integrating DCIP data with sparse but accurate borehole data results in a high resolution characterization of the SZA.

Integrating hydraulic tomography, electrical resistivity tomography, and partitioning interwell tracer test datasets to improve identification of pool-dominated DNAPL source zone architecture.

High-resolution characterization of complex dense non-aqueous phase liquid (DNAPL) contaminated sites is crucial for developing effective remediation strategies. The DNAPL source zone is usually characterized by hydraulic/partitioning tracer tomography (HPTT). However, the HPTT method may fail to capture the highly saturated pool-dominated DNAPL source zone architecture (SZA), because partitioning tracers tend to bypass the low-permeability zones where the pool DNAPL accumulates, resulting in a low-resolution DNAPL estimation. With a limited number of measurements, the estimation errors may be significant. To overcome these difficulties, time-lapse electrical resistivity tomography (ERT) was integrated with the partitioning interwell tracer test (PITT) and hydraulic tomography (HT) to characterize the pool-dominated DNAPL SZA. Herein, we proposed an iterative joint inversion framework coupling the multiphase flow model with the ERT forward model to estimate the heterogeneous permeability distribution and DNAPL SZA. Under this framework, permeability was estimated using the hydraulic head data from HT in stage 1, and the DNAPL SZA was subsequently estimated by assimilating both the PITT and ERT observations in stage 2. The permeability estimated from stage 1 was used as prior information for stage 2, and the DNAPL saturation estimated from stage 2 was served as prior information for stage 1 in the next loop to form an iterative loop to improve the estimation of both permeability and DNAPL SZA. The iterative joint inversion framework was evaluated in two numerical experiments with different heterogeneous structures by assimilating multi-source datasets, including hydraulic head, partitioning interwell tracer concentration, and electrical resistivity. Results show that with limited measurements of HPTT method, one can roughly capture the DNAPL distribution, missing the fine structure of the DNAPL SZA. In contrast, by incorporating multi-source datasets, the heterogeneous permeability and DNAPL SZA can be reconstructed with a higher resolution. Furthermore, the inversion accuracy of the DNAPL SZA improves progressively as the iteration proceeds, which demonstrates the advantage of utilizing complementary information from permeability and DNAPL distribution through the iteration framework. Comparing with the results without loop iteration, the estimation error is reduced by 17.3% for permeability and 8.6% for DNAPL saturation in Experiment 1; by 14.7% for permeability and 11.2% for DNAPL saturation in Experiment 2 through the iterative framework. To further evaluate our framework, we preformed the prediction of the depletion process of the DNAPL source zone and plume based on the estimated DNAPL SZA. Results show that using the iterative framework, the prediction of the SZA depletion is greatly improved, i.e., the estimation error of the dissolved downstream plume from the DNAPL source zone after 3 years is reduced by 20.9% in Experiment 1, and by 43.2% in Experiment 2, respectively, through the iterative framework. This significant improvement is because the iterative method can better capture the spread of DNAPL pool.