Publications

Mercury intrusion porosimetry (MIP) is an often-applied technique for determining pore throat distributions and seal analysis of fine-grained rocks. Due to closure effects, potential pore collapse, and complex pore network topologies, MIP data interpretation can be ambiguous, and often biased toward smaller pores in the distribution. We apply 3D imaging techniques and lattice-Boltzmann modeling in interpreting MIP data for samples of the Cretaceous Selma Group Chalk. In the Mississippi Interior Salt Basin, the Selma Chalk is the apparent seal for oil and gas fields in the underlying Eutaw Fm., and, where unfractured, the Selma Chalk is one of the regional-scale seals identified by the Southeast Regional Carbon Sequestration Partnership for CO2 injection sites. Dual focused ion - scanning electron beam and laser scanning confocal microscopy methods are used for 3D imaging of nanometer-to-micron scale microcrack and pore distributions in the Selma Chalk. A combination of image analysis software is used to obtain geometric pore body and throat distributions and other topological properties, which are compared to MIP results. 3D data sets of pore-microfracture networks are used in Lattice Boltzmann simulations of drainage (wetting fluid displaced by non-wetting fluid via the Shan-Chen algorithm), which in turn are used to model MIP procedures. Results are used in interpreting MIP results, understanding microfracture-matrix interaction during multiphase flow, and seal analysis for underground CO2 storage.

Dissolved CO2 from geological CO2 sequestration may react with dissolved minerals in fractured rocks or confined aquifers and cause mineral precipitation. The overall rate of reaction can be limited by diffusive or dispersive mixing, and mineral precipitation can block pores and further hinder these processes. Mixing-induced calcite precipitation experiments were performed by injecting solutions containing CaCl2 and Na2CO3 through two separate inlets of a micromodel (1-cm x 2-cm x 40-microns); transverse dispersion caused the two solutions to mix along the center of the micromodel, resulting in calcite precipitation. The amount of calcite precipitation initially increased to a maximum and then decreased to a steady state value. Fluorescent microscopy and imaging techniques were used to visualize calcite precipitation, and the corresponding effects on the flow field. Experimental micromodel results were evaluated with pore-scale simulations using a 2-D Lattice-Boltzmann code for water flow and a finite volume code for reactive transport. The reactive transport model included the impact of pH upon carbonate speciation and calcite dissolution. We found that proper estimation of the effective diffusion coefficient and the reaction surface area is necessary to adequately simulate precipitation and dissolution rates. The effective diffusion coefficient was decreased in grid cells where calcite precipitated, and keeping track of reactive surface over time played a significant role in predicting reaction patterns. Our results may improve understanding of the fundamental physicochemical processes during CO2 sequestration in geologic formations.

Heterogeneity plays an important role in groundwater flow and contaminant transport in natural systems. Since it is impossible to directly measure spatial variability of hydraulic conductivity, predictions of solute transport based on mathematical models are always uncertain. While in most cases groundwater flow and tracer transport problems are investigated in two-dimensional (2D) systems, it is important to study more realistic and well-controlled 3D systems to fully evaluate inverse parameter estimation techniques and evaluate uncertainty in the resulting estimates. We used tracer concentration breakthrough curves (BTCs) obtained from a magnetic resonance imaging (MRI) technique in a small flow cell (14 x 8 x 8 cm) that was packed with a known pattern of five different sands (i.e., zones) having cm-scale variability. In contrast to typical inversion systems with head, conductivity and concentration measurements at limited points, the MRI data included BTCs measured at a voxel scale ({approx}0.2 cm in each dimension) over 13 x 8 x 8 cm with a well controlled boundary condition, but did not have direct measurements of head and conductivity. Hydraulic conductivity and porosity were conceptualized as spatial random fields and estimated using pilot points along layers of the 3D medium. The steady state water flow and solute transport were solved using MODFLOW and MODPATH. The inversion problem was solved with a nonlinear parameter estimation package - PEST. Two approaches to parameterization of the spatial fields are evaluated: (1) The detailed zone information was used as prior information to constrain the spatial impact of the pilot points and reduce the number of parameters; and (2) highly parameterized inversion at cm scale (e.g., 1664 parameters) using singular value decomposition (SVD) methodology to significantly reduce the run-time demands. Both results will be compared to measured BTCs. With MRI, it is easy to change the averaging scale of the observed concentration from point to cross-section. This comparison allows us to evaluate which method best matches experimental results at different scales. To evaluate the uncertainty in parameter estimation, the null space Monte Carlo method will be used to reduce computational burden of the development of calibration-constrained Monte Carlo based parameter fields. This study will illustrate how accurately a well-calibrated model can predict contaminant transport.