Parameterization and Validation of Numerical Transport Models using Hydrogeophysical Estimation Approaches

Abstract

Numerical modeling of fluid flow and transport is often used to test hypotheses and to guide resource management. For example, numerical models are used to simulate contaminant plume transport over time and to design effective remediation plans, and to simulate aquifer depletion versus recharge over time and to design sustainable resource extraction programs. Effective hydrological hypothesis testing using mathematical modeling requires accurate and sufficient parameterization. It is well recognized that physical and chemical properties of aquifer systems (such as hydraulic conductivity and reactive iron oxide surface area) is great and can have multiple scales of spatial variability. As these parameters exert significant control on transport, natural attenuation, and active remediation processes, successful modeling requires sufficient information about the distribution of these properties. Given the difficulty of obtaining characterization data for model parameterization using conventional wellbore approaches, the predictive capability of these models for practically guiding water resource or contaminant management is limited. Geophysical methods hold potential for providing quantitative information about subsurface hydrogeological parameters or processes that can be used to constrain or validate transport models. However, to use integrated geophysical and hydrogeological information for such purposes, we must confront issues of scale, non-uniqueness, and uncertainty; we must have petrophysical relationships available that link the disparate measurements; we must have an understanding about the spatial distribution of measurement and parameter estimate uncertainties; and we must have estimation approaches that can be used for integration. In this presentation, we will first review examples of the estimation of hydrological-geochemical properties needed for model parameterization using geophysical data and stochastic approaches, such as Bayesian and Monte Carlo Markov Chain. These methods are advantageous in that they offer a systematic approach for incorporating different types of data and petrophysical relationships, and provide estimates of uncertainty that can be used within transport simulation. We also illustrate estimation examples performed using both sequential processes (whereby the geophysical data are interpreted and converted to a form in which they can act as a constraint on the hydrogeological inversion) and joint inversion processes, and describe the benefits and limitations of the different approaches. Advanced numerical models are being developed or used to simulate coupled biogeochemical-hydrological processes, such as dissolution and precipitation of minerals, gas evolution, changes in soil water and oxygen levels, sorption, attachment/detachment, oxidation and reduction, biofilm generation, and changes in permeability and porosity. We will review several examples of the use of geophysically-obtained parameter estimates to parameterize or validate numerical flow and transport models. In the first set of examples, we will illustrate how geophysically obtained estimates of aquifer zonation, hydraulic conductivity, and sediment FeII and FeIII concentrations were used within models to explore the impact of physical and chemical heterogeneity on chemical, bacteria, and uranium transport and reduction processes. As a final example, we will illustrate the use of time-lapse geophysical data for assisting in the understanding of the spatiotemporal products associated with active remediation approaches (such as the generation of gasses and precipitates or the changes in aqueous concentrations). We will discuss how the dense geophysical information can be used to explore the capabilities of advanced numerical models to accurately account for coupled physical, chemical, and biological processes in the presence of natural heterogeneity at the field scale.