Philip D. Meyer

Changes in hydrologic properties of aquifer media due to chemical reactions: A review

Hydrologic properties that govern fluid flow through the subsurface are porosity, permeability, relative permeability, fluid-fluid and fluid-solid interfacial areas, pore and particle size distributions, which may change due to dissolution/precipitation of minerals, fine particle release and capture, ion exchange, and clay swelling. Provided here is a review on the change of hydrologic properties in subsurface media due to chemical processes, and the modeling of such changes. Precipitation and dissolution processes affecting the hydrologic properties, their kinetics and the effect of hydrodynamic factors on such processes are discussed. Precipitation in carbonaceous, siliceous, alkaline and acidic environments, and the role of dissolution and clay swelling in formation damage are reviewed. Changes in properties of unsaturated and fractured media were also discussed. Traditionally, different approaches were used to model various physico-chemical processes and their effect on the hydrologic properties. A detailed review of these methods, including the geochemical equilibrium and kinetic models, chemical divide pathway models, flow and transport models, precipitation/dissolution wave theory, network models, porosity and permeability reduction models, is presented. Recommendations are provided for the assessment of changes in the hydrologic properties of subsurface media attributable to chemical reactions, and modeling flow and transport in their presence. Further, research needs on the changes in hydrologic properties and constitutive relationships among such properties in unsaturated media are identified.

Assessment of parametric uncertainty for groundwater reactive transport modeling

The validity of using Gaussian assumptions for model residuals in uncertainty quantification of a groundwater reactive transport model was evaluated in this study. Least squares regression methods explicitly assume Gaussian residuals, and the assumption leads to Gaussian likelihood functions, model parameters, and model predictions. While the Bayesian methods do not explicitly require the Gaussian assumption, Gaussian residuals are widely used. This paper shows that the residuals of the reactive transport model are non-Gaussian, heteroscedastic, and correlated in time; characterizing them requires using a generalized likelihood function such as the formal generalized likelihood function developed by Schoups and Vrugt (2010). For the surface complexation model considered in this study for simulating uranium reactive transport in groundwater, parametric uncertainty is quantified using the least squares regression methods and Bayesian methods with both Gaussian and formal generalized likelihood functions. While the least squares methods and Bayesian methods with Gaussian likelihood function produce similar Gaussian parameter distributions, the parameter distributions of Bayesian uncertainty quantification using the formal generalized likelihood function are non-Gaussian. In addition, predictive performance of formal generalized likelihood function is superior to that of least squares regression and Bayesian methods with Gaussian likelihood function. The Bayesian uncertainty quantification is conducted using the differential evolution adaptive metropolis (DREAM(zs)) algorithm; as a Markov chain Monte Carlo (MCMC) method, it is a robust tool for quantifying uncertainty in groundwater reactive transport models. For the surface complexation model, the regression-based local sensitivity analysis and Morris- and DREAM(ZS)-based global sensitivity analysis yield almost identical ranking of parameter importance. The uncertainty analysis may help select appropriate likelihood functions, improve model calibration, and reduce predictive uncertainty in other groundwater reactive transport and environmental modeling.

Greedy Heuristic Methods for Locating Water Quality Sensors in Distribution Systems

Monitoring and surveillance systems for drinking water distribution networks are intended to provide real time warning of drinking water contamination events and mitigate their public health consequences. Drinking water distribution networks often serve large populations over vast areas. There exist a large number of access points where contaminants could be introduced, and these are spread throughout the service area. Transport of contaminants from these access points to consumers would occur through a multitude of pathways, and be dominated by water flows that change magnitude and direction in response to frequent changes in water use and system operation. The above features of drinking water distribution networks dictate that design of a successful monitoring and surveillance system is comprised of three interrelated sub-tasks:

Comment on “Inverse groundwater modeling for hydraulic conductivity estimation using Bayesian model averaging and variance window” by Frank T.-C. Tsai and Xiaobao Li

[1] Tsai and Li [2008] assert that the Bayesian information criterion (BIC) [Schwarz, 1978] is better suited for comparing models having different parameters than is the Kashyap criterion (KIC) [Kashyap, 1982] because a Fisher information term in the latter may rank models with relatively large parameter estimation uncertainties higher than other models. We start by noting that KIC reduces asymptotically to BIC as the number of observations becomes large relative to the number of adjustable model parameters [Ye et al., 2008]. If Tsai and Li [2008] were correct in their assertion, this would imply that it is better to treat a finite set of data as if it were theoretically infinite, a proposition that is logically unappealing and not necessary in practice. [2] The Fisher information term imbues KIC with desirable model selection properties not shared by BIC [Ye et al., 2008]: it sometimes prefers more complex models than does BIC because of its unique ability to discriminate between models not only on the basis of their goodness of fit to observational data and number of parameters but also on the quality of the available data and of the parameter estimates. To appreciate this role of the Fisher information term, it must not be considered in isolation as do Tsai and Li [2008] but rather in the context of all terms entering into KIC as do Ye et al. [2008]. The purpose of this comment is to elaborate on the discussion of Ye et al. [2008] by explaining further why the tendency of KIC to prefer models with relatively large parameter estimation uncertainty is a strength rather than a weakness. [3] In a manner analogous to that of Sivia and Skilling [2006], we present a simple example which helps elucidate the role played by the Fisher information term in KIC and allows us to offer general observations regarding more complex applications, such as the groundwater inverse modeling analysis of Tsai and Li [2008]. Consider two models, A and B, having one adjustable parameter each, m and l, respectively. Bayes’ theorem implies that the ratio between the posterior model probabilities, conditioned on an observation vector D, is

Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty

The objective of the research described in this report is the development and application of a methodology for comprehensively assessing the hydrogeologic uncertainties involved in dose assessment, including uncertainties associated with conceptual models, parameters, and scenarios. This report describes and applies a statistical method to quantitatively estimate the combined uncertainty in model predictions arising from conceptual model and parameter uncertainties. The method relies on model averaging to combine the predictions of a set of alternative models. Implementation is driven by the available data. When there is minimal site-specific data the method can be carried out with prior parameter estimates based on generic data and subjective prior model probabilities. For sites with observations of system behavior (and optionally data characterizing model parameters), the method uses model calibration to update the prior parameter estimates and model probabilities based on the correspondence between model predictions and site observations. The set of model alternatives can contain both simplified and complex models, with the requirement that all models be based on the same set of data. The method was applied to the geostatistical modeling of air permeability at a fractured rock site. Seven alternative variogram models of log air permeability were considered to represent data from single-hole pneumatic injection tests in six boreholes at the site. Unbiased maximum likelihood estimates of variogram and drift parameters were obtained for each model. Standard information criteria provided an ambiguous ranking of the models, which would not justify selecting one of them and discarding all others as is commonly done in practice. Instead, some of the models were eliminated based on their negligibly small updated probabilities and the rest were used to project the measured log permeabilities by kriging onto a rock volume containing the six boreholes. These four projections, and associated kriging variances, were averaged using the posterior model probabilities as weights. Finally, cross-validation was conducted by eliminating from consideration all data from one borehole at a time, repeating the above process, and comparing the predictive capability of the model-averaged result with that of each individual model. Using two quantitative measures of comparison, the model-averaged result was superior to any individual geostatistical model of log permeability considered.

Influence of mineral precipitation and dissolution on hydrologic properties of porous media in static and dynamic systems

A critical component in determining the suitability of disposing glassified, low activity waste is the identification of key mineral assemblages affecting the porosity and permeability of both the glass and near- and far-field materials. In this study, two different classes of geochemical models are used to identify mineral precipitation and dissolution potentials for an immobilized low-activity waste (ILAW) disposal facility in Hanford, Washington. The first is a static geochemical model that does not consider the effects of transport. The second model is dynamic, and combines geochemical reactions with hydrogeological processes such as advection, diffusion and dispersion. This reactive transport model also includes an innovative application of a depositional film model for determining changes in permeability due to mineral precipitation and dissolution reactions. Although both models describe solid-aqueous phase reactions kinetically, the two models identify two different sets of mineral assemblages affecting the porosity and permeability of the media. These markedly different results are due to transport considerations, the most significant of which are the spatial variability in aqueous concentrations, and advection and diffusion of dissolved glass constituents into the backfill materials. This work shows that for the prediction of geochemical behavior of engineered systems, such as the ILAW disposal facility, the traditional reaction path modeling approach is not sufficient for an accurate assessment of the precipitation of key mineral assemblages and their effect on the geochemical and hydraulic behavior of the waste glass. Reactive transport modeling improves this assessment significantly. The static model is useful in identifying potential minerals to be included in the reactive transport simulations. The dynamic model, however, ultimately determines the key mineral assemblages affecting both the geochemical behavior and the hydraulic properties of the waste glass in the presence of a flowing aqueous phase.

Information on Hydrologic Conceptual Models, Parameters, Uncertainty Analysis, and Data Sources for Dose Assessments at Decommissioning Sites

This report addresses issues related to the analysis of uncertainty in dose assessments conducted as part of decommissioning analyses. The analysis is limited to the hydrologic aspects of the exposure pathway involving infiltration of water at the ground surface, leaching of contaminants, and transport of contaminants through the groundwater to a point of exposure. The basic conceptual models and mathematical implementations of three dose assessment codes are outlined along with the site-specific conditions under which the codes may provide inaccurate, potentially nonconservative results. In addition, the hydrologic parameters of the codes are identified and compared. A methodology for parameter uncertainty assessment is outlined that considers the potential data limitations and modeling needs of decommissioning analyses. This methodology uses generic parameter distributions based on national or regional databases, sensitivity analysis, probabilistic modeling, and Bayesian updating to incorporate site-specific information. Data sources for best-estimate parameter values and parameter uncertainty information are also reviewed. A follow-on report will illustrate the uncertainty assessment methodology using decommissioning test cases.

Test Plan for Field Experiments to Support the Immobilized Low-Activity Waste Disposal Performance Assessment at the Hanford Site

Much of the data collected to support the Immobilized Low-Activity Waste Performance Assessment (ILAW PA) simulations have been obtained in the laboratory on a relatively small scale (less than 10 cm). In addition, the PA simulations themselves are currently the only means available to integrate the chemical and hydrologic processes involved in the transport of contaminants from the disposal facility into the environment. This report describes the test plan for field experiments to provide data on the hydraulic, transport, and geochemical characteristics of the near-field materials on a more representative (i.e., larger) scale than the laboratory data currently available. The experiments will also provide results that encompass a variety of transport processes likely to occur within the actual disposal facility. These experiments will thus provide the first integrated data on the ILAW facility performance and will provide a crucial dataset to evaluate the simulation-based estimates of overall facility performance used in the PA.

Implementation of biofilm permeability models for mineral reactions in saturated porous media

An approach based on continuous biofilm models is proposed for modeling permeability changes due to mineral precipitation and dissolution in saturated porous media. In contrast to the biofilm approach, implementation of the film depositional models within a reactive transport code requires a time-dependent calculation of the mineral films in the pore space. Two different methods for this calculation are investigated. The first method assumes a direct relationship between changes in mineral radii (i.e., surface area) and changes in the pore space. In the second method, an effective change in pore radii is calculated based on the relationship between permeability and grain size. Porous media permeability is determined by coupling the film permeability models (Mualem and Childs and Collis-George) to a volumetric model that incorporates both mineral density and reactive surface area. Results from single mineral dissolution and single mineral precipitation simulations provide reasonable estimates of permeability, though they predict smaller permeability changes relative to the Kozeny and Carmen model. However, a comparison of experimental and simulated data show that the Mualem film model is the only one that can replicate the oscillations in permeability that occur as a result of simultaneous dissolution and precipitation reactions occurring within the porous media.

Analysis of Hydrogeologic Conceptual Model and Parameter Uncertainty

A systematic methodology for assessing hydrogeologic conceptual model, parameter, and scenario uncertainties is being developed to support technical reviews of environmental assessments related to decommissioning of nuclear facilities. The first major task being undertaken is to produce a coupled parameter and conceptual model uncertainty assessment methodology. This task is based on previous studies that have primarily dealt individually with these two types of uncertainties. Conceptual model uncertainty analysis is based on the existence of alternative conceptual models that are generated using a set of clearly stated guidelines targeted at the needs of NRC staff. Parameter uncertainty analysis makes use of generic site characterization data as well as site-specific characterization and monitoring data to evaluate parameter uncertainty in each of the alternative conceptual models. Propagation of parameter uncertainty will be carried out through implementation of a general stochastic model of groundwater flow and transport in the saturated and unsaturated zones. Evaluation of prediction uncertainty will make use of Bayesian model averaging and visualization of model results. The goal of this study is to develop a practical tool to quantify uncertainties in the conceptual model and parameters identified in performance assessments.