Spatial and temporal quantification of nanoparticle transport in geologic porous media
Silica nanoparticles exist in the environment due to increasing utilization in consumer products, lubricants, and production during certain manufacturing and combustion processes. In addition to their undesirable—and potentially hazardous presence in the environment—nanoparticles are rapidly emerging as a tool for fluid flow alteration in the subsurface, and for removal of organic pollutants, toxic metals, uranium, and phosphate from contaminated water. Nanoparticle transport and retention in simplified porous media has been well constrained and mechanistically described. However, we lack quantitative understanding and prediction in dynamic non-ideal systems where nanoparticles are influenced by a complex combination of inconsistent hydrodynamic forces and physical and chemical heterogeneity. The goal of this project is to use novel experimental methods to develop a better understanding of in situ nanoparticle transport in geologic media. Specifically, we aim to use in situ imaging of nanoparticle transport and retention to improve mechanistic understanding and modeling predictions of the flow and fate of nanoparticles in permeable geologic media.
Transport and fate of PFAS in sediments
Per- and polyfluoroalkyl substances (PFAS) are a diverse group of fluorinated organic contaminants in the environment resulting from use at firefighting facilities, in consumer products, waste at manufacturing sites, and disposal in landfills. PFAS mobility in the subsurface is difficult to predict due to adsorption behavior that varies as a function of PFAS compound chemistry, fluid chemistry, subsurface mineralogy, and hydrogeological conditions. The adsorption behavior is especially complex under multiphase flow conditions due to the unique wetting properties of PFAS. The goal of this project is to use innovative experimental methods to better understand and model the transport of PFAS from surface releases into the subsurface.
Statistical and Deep Learning Approaches for Permeability Inversion Using Massive Positron Emission Tomography Datasets
A positron emission tomography image at a single timestep often consists of over 10,000 concentration measurements of the radiolabeled substance present in a sediment column or geologic core. These massive time-lapse datasets are possible due to the millimeter-scale discretization, termed ‘voxels’, of positron emission tomography (PET) images. With PET imaging, it is now possible to acquire in situ measurements of a wide range of radiolabeled fluids, gases, and nanoparticles within a discretized 3D spatial domain. The imaging method enables us to generate massive volumes of data not typically available from traditional hydrogeologic laboratory or field approaches. What is not known is how to fully maximize the value of these massive time-lapse datasets for multi-scale model parameterization, as this requires a paradigm shift from data interpretation and inversion approaches designed for small sparse datasets. The objective of this project is to develop mathematical algorithms to calculate multi-scale permeability heterogeneity in geologic core samples using positron emission tomography datasets collected from experiments in a wide range of geologic samples.
Solute transport during spontaneous imbibition
The focus of this project is to utilize positron emission tomography (PET) and X-ray computed tomography (CT) to provide a window into complex flow and transport processes at the sub-core scale. These in-situ imaging techniques are specifically used to quantify time-lapse fluid displacement and solute advection during spontaneous imbibition under elevated pressure conditions. For example, the image to the lower left shows the water saturation measured with CT, while the image to the lower right illustrates the solute distribution measured with PET. The images are taken during equivalent times during repeated experiments. We have found that experimentally observed solute accumulation during spontaneous imbibition is significantly different from solute advection behavior observed during water injection under saturated conditions. A coupled 2D numerical model is being built to explain how sub-core heterogeneity controls solute migration in the imbibition front. These observations of dynamic, capillary-driven fluid displacement processes help improve our understanding of potential remobilization of CO2 in carbon sequestration projects and non-aqueous phase liquid contaminant remediation.