We have used the TOUGH2-MP/ECO2N code to perform numerical simulation studies of the long-term behavior of CO{sub 2} stored in an aquifer with a sloping caprock. This problem is of great practical interest, and is very challenging due to the importance of multi-scale processes. We find that the mechanism of plume advance is different from what is seen in a forced immiscible displacement, such as gas injection into a water-saturated medium. Instead of pushing the water forward, the plume advances because the vertical pressure gradients within the plume are smaller than hydrostatic, causing the groundwater column to collapse ahead of the plume tip. Increased resistance to vertical flow of aqueous phase in anisotropic media leads to reduced speed of updip plume advancement. Vertical equilibrium models that ignore effects of vertical flow will overpredict the speed of plume advancement. The CO{sub 2} plume becomes thinner as it advances, yet the speed of advancement remains constant over the entire simulation period of up to 400 years, with migration distances of more than 80 km. Our simulations include dissolution of CO{sub 2} into the aqueous phase and associated density increase, and molecular diffusion. However, no convection develops in the aqueous phase because it …

Some diagnostics at the National Ignition Facility (NIF), including the Gamma Reaction History (GRH) diagnostic, require multiple channels of data to achieve the required dynamic range. These channels need to be stitched together into a single time series, and they may have non-uniform and redundant time samples. We chose to apply the popular cubic smoothing spline technique to our stitching problem because we needed a general non-parametric method. We adapted one of the algorithms in the literature, by Hutchinson and deHoog, to our needs. The modified algorithm and the resulting code perform a cubic smoothing spline fit to multiple data channels with redundant time samples and missing data points. The data channels can have different, time-varying, zero-mean white noise characteristics. The method we employ automatically determines an optimal smoothing level by minimizing the Generalized Cross Validation (GCV) score. In order to automatically validate the smoothing level selection, the Weighted Sum-Squared Residual (WSSR) and zero-mean tests are performed on the residuals. Further, confidence intervals, both analytical and Monte Carlo, are also calculated. In this paper, we describe the derivation of our cubic smoothing spline algorithm. We outline the algorithm and test it with simulated and experimental data.