Comparison of Upscaled Models for Multistage Mass Discharge from DNAPL Source Zones

Analytical upscaled models that can describe the depletion of dense nonaqueous phase liquids (DNAPLs) and the associated mass discharge are a practical alternative to computationally demanding and data-intensive multiphase numerical simulators. A major shortcoming of most existing upscaled models is that they cannot reproduce the nonmonotonic, multistage effluent concentrations often observed in experiments and numerical simulations. Upscaled models that can produce multistage concentrations either require calibration, which increases the cost of applying them in the field, or use dual-domain conceptual models that may not apply for spatially complex source zones. In this study, a new upscaled model is presented that can describe the nonmonotonic, multistage average concentrations emanating from complex DNAPL source zones. This is achieved by explicitly considering the temporal evolution of three source zone parameters, namely source zone projected area, the average of local-scale DNAPL saturations, and the average of local-scale aqueous relative permeability, without using empirical parameters. The model is evaluated for two real and twelve hypothetical centimeter-scale complex source zones. The proposed model captures the temporal variations in concentrations better than an empirical model and a dual-domain ganglia- to-pool ratio model. The results provide evidence that effluent concentrations downgradient of DNAPL source zones are controlled by the evolution of the aforementioned macroscopic parameters. This knowledge can be useful for the interpretation of field observations of effluent concentrations downstream of DNAPL source zones, and for the development of predictive upscaled models. Advances in DNAPL characterization techniques are needed to quantify these macroscopic parameters that can be used to guide DNAPL remediation efforts

Smoothing-based Compressed State Kalman Filter for Joint State-parameter Estimation: Applications in Reservoir Characterization and CO2 Storage Monitoring

The operation of most engineered hydrogeological systems relies on simulating physical processes using numerical models with uncertain parameters and initial conditions. Predictions by such uncertain models can be greatly improved by Kalman-filter techniques that sequentially assimilate monitoring data. Each assimilation constitutes a nonlinear optimization, which is solved by linearizing an objective function about the model prediction and applying a linear correction to this prediction. However, if model parameters and initial conditions are uncertain, the optimization problem becomes strongly nonlinear and a linear correction may yield unphysical results. In this paper, we investigate the utility of one-step ahead smoothing, a variant of the traditional filtering process, to eliminate nonphysical results and reduce estimation artifacts caused by nonlinearities. We present the smoothing-based compressed state Kalman filter (sCSKF), an algorithm that combines one step ahead smoothing, in which current observations are used to correct the state and parameters one step back in time, with a nonensemble covariance compression scheme, that reduces the computational cost by efficiently exploring the high-dimensional state and parameter space. Numerical experiments show that when model parameters are uncertain and the states exhibit hyperbolic behavior with sharp fronts, as in CO2 storage applications, one-step ahead smoothing reduces overshooting errors and, by design, gives physically consistent state and parameter estimates. We compared sCSKF with commonly used data assimilation methods and showed that for the same computational cost, combining one step ahead smoothing and nonensemble compression is advantageous for real-time characterization and monitoring of large-scale hydrogeological systems with sharp moving fronts

Coupled Simulation of DNAPL Infiltration and Dissolution in Three-Dimensional Heterogeneous Domains: Process Model Validation

A three-dimensional multiphase numerical model was used to simulate the infiltration and dissolution of a dense nonaqueous phase liquid (DNAPL) release in two experimental flow cells containing different heterogeneous and well-characterized permeability fields. DNAPL infiltration was modeled using Brooks-Corey-Burdine hysteretic constitutive relationships. DNAPL dissolution was simulated using a rate-limited mass transfer expression with a velocity-dependent mass transfer coefficient and a thermodynamically based calculation of DNAPL-water interfacial area. The model did not require calibration of any parameters. The model predictions were compared to experimental measurements of high-resolution DNAPL saturations and effluent concentrations. The predicted concentrations were in close agreement with measurements for both domains, indicating that important processes were effectively captured by the model. DNAPL saturations greatly influenced mass transfer rates through their effect on relative permeability and velocity. Areas with low DNAPL saturation were associated with low interfacial areas, which resulted in reduced mass transfer rates and nonequilibrium dissolution. This was captured by the thermodynamic interfacial area model, while a geometric model overestimated the interfacial areas and the overall mass transfer. This study presents the first validation of the thermodynamic dissolution model in three dimensions and for high aqueous phase velocities; such conditions are typical for remediation operations, especially in heterogeneous aquifers. The demonstrated ability to predict DNAPL dissolution, only requiring prior characterization of soil properties and DNAPL release conditions, represents a significant improvement compared to empirical dissolution models and provides an opportunity to delineate the relationship between source zone architecture and the remediation potential for complex DNAPL source zones

The Compressed State Kalman Filter for Nonlinear State Estimation: Application to Large-Scale Reservoir Monitoring

Reservoir monitoring aims to provide snapshots of reservoir conditions and their uncertainties to assist operation management and risk analysis. These snapshots may contain millions of state variables, e.g., pressures and saturations, which can be estimated by assimilating data in real time using the Kalman filter (KF). However, the KF has a computational cost that scales quadratically with the number of unknowns, m, due to the cost of computing and storing the covariance and Jacobian matrices, along with their products. The compressed state Kalman filter (CSKF) adapts the KF for solving large-scale monitoring problems. The CSKF uses N preselected orthogonal bases to compute an accurate rank-N approximation of the covariance that is close to the optimal spectral approximation given by SVD. The CSKF has a computational cost that scales linearly in m and uses an efficient matrix-free approach that propagates uncertainties using N + 1 forward model evaluations, where . Here we present a generalized CSKF algorithm for nonlinear state estimation problems such as CO2 monitoring. For simultaneous estimation of multiple types of state variables, the algorithm allows selecting bases that represent the variability of each state type. Through synthetic numerical experiments of CO2 monitoring, we show that the CSKF can reproduce the Kalman gain accurately even for large compression ratios (m/N). For a given computational cost, the CSKF uses a robust and flexible compression scheme that gives more reliable uncertainty estimates than the ensemble Kalman filter, which may display loss of ensemble variability leading to suboptimal uncertainty estimates

Insights and Modelling Tools for Designing and Improving Chlorinated Solvent Bioremediation Applications

The chlorinated solvents tetrachloroethene (PCE) and trichloroethene (TCE) have been used extensively in industry and are now amongst the most common and hazardous groundwater contaminants. These solvents are typically present as dense, non-aqueous phase liquids (DNAPLs) and represent long-term source zones that produce persistent contamination plumes in aquifers. Under anaerobic conditions, chlorinated ethenes may be biodegraded via reductive dechlorination (the biologically mediated, step-wise removal of chlorine) to form ethene, a relatively innocuous end-product. The rate of reductive dechlorination can be enhanced by stimulating the activity of dechlorinating bacteria by injection of an electron donor (typically an organic substrate that generates hydrogen upon fermentation), nutrients and, in some cases, microbial communities known to dechlorinate effectively to ethene (i.e., bioaugmentation). Reductive dechlorination has been shown to be a viable technology for in situ treatment of dissolved chlorinated solvent plumes, and recent laboratory studies have suggested that this strategy may also be effective for chlorinated solvent DNAPL. Here, the source zone is targeted directly, with the aim of reducing its lifespan by enhancing dissolution from the DNAPL and sorbed phases and coupling this with effective and sustained dechlorination near the DNAPL-water interface and within the plume. This bulletin focuses on modelling of enhanced dechlorination processes in groundwater, including the modelling tools developed in the SABRE project (under which this report was written, http://www.claire.co.uk/index.php?option=com_content&task=view&id=53&Itemid=47&showall=1), insights gained from the models concerning factors controlling the rates and extent of enhanced source zone DNAPL bioremediation, and how the modelling tools can be used to assist future applications of this technology

Modelling of dissolution and bioremediation of chlorinated ethene DNAPL source zones

This thesis investigated the dissolution of dense non aqueous phase liquids (DNAPL) source zones in the subsurface and the effectiveness of enhanced bioremediation for the treatment of chlorinated ethene DNAPLs, using numerical modeling. For this purpose, an existing multiphase numerical model was extended to include comprehensive models for the processes of dissolution and reaction. The first part of the thesis examined DNAPL dissolution. First, a thermodynamic-based dissolution model was validated using experimental data from two complex heterogeneous DNAPL releases. Model predictions for DNAPL spatial distribution and effluent concentrations agreed well with experimental measurements, without requiring calibration. This is the first successful application of a predictive dissolution model in the literature. Model results showed the important effects of relative permeability and interfacial areas on dissolution rates. Then, the thermodynamic dissolution model was compared to simpler models typically used in the literature. Five Sherwood-Gilland (SG) empirical correlations were evaluated and their limitations were illustrated. A new dissolution model was proposed that combined the predictive ability of the thermodynamic model and the simplicity of SG models, and is applicable for complex source zones. Lastly, the relationship between the DNAPL source architecture and downstream concentrations was investigated, focusing on multistage concentration profiles. A new upscaled model was proposed that is able to capture such complex behavior. In the second part of this thesis the thermodynamic dissolution model was combined with a model for reductive dechlorination of chlorinated ethenes to simulate DNAPL bioremediation. Simulations were conducted for simple DNAPL source zones to investigate the impact of dissolution-related processes on bioremediation effectiveness. Dissolution kinetics and back-partitioning of daughter products in the DNAPL were shown to affect dechlorination. Then, the investigation was extended to DNAPL source zones of complex architectures in heterogeneous domains, illustrating the importance of the source zone architecture for the effectiveness of DNAPL bioremediation. Overall, this thesis presents a comprehensive numerical model that will be an important research tool for evaluating the effectiveness of in-situ bioremediation for DNAPL source zones, and will provide the means for a better understanding and control of the critical factors affecting this technology in the field.Ph

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

International audienceToxic 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