Mechanistic Modeling of Subsurface Multifluid Flow and Biogeochemical Reactive Transport
EMSL Project ID
35417
Abstract
A reliable means of quantitatively predicting fluid movement and biogeochemistry in the subsurface environment is of significant value to the Department of Energy (DOE) and to our nation as a whole. This capability is critical to the development of scientifically defensible decisions that serve DOE's long-term missions of energy security and the protection of human health and the environment. The proposed work addresses the development of a next generation highly scalable subsurface simulation capability and its application to mechanisms controlling long-term persistence of contaminant plumes, geologic sequestration of carbon, and subsurface bioremediation of metal waste. The collective activities are directed at the goal of a quantitatively mechanistic predictive understanding of multifluid flow and multicomponent biogeochemical reactive transport in complex subsurface systems. Massively parallel computing is essential to the science-driven need for increasing resolution of process and property detail, linking more spatial and temporal scales, coupling more detailed processes, and testing larger ranges of modeling scenarios. This capability will be the foundation for new lines of research allowing more and different kinds of data to estimate distributed model parameters and quantify uncertainty. A key focus of the algorithm and code development is scalable performance on current and future extreme scale computing architectures.
In conjunction with laboratory and field studies performed for the DOE Offices of Science and Fossil Energy, this project uses massively parallel computing to make progress on important subsurface science and engineering issues: (1) scale-up of rate-limited mass transfer processes from pore to field scales, (2) mixing and physical trapping of injected supercritical CO2 in heterogeneous geologic formations; (3) the interplay of physical, geochemical, and microbiological processes during in situ uranium bioremediation, and (4) fault-tolerant parameter estimation and uncertainty for multivariate inverse modeling based on property transfer and petrophysical models. Underlying the mathematical modeling of the subsurface processes in this project is an equally strong commitment to continuously improving the algorithms, computational methods, and computer science that are the foundation for robust, accurate, efficient, portable, and scalable subsurface simulation software.
Project Details
Project type
Capability Research
Start Date
2009-10-14
End Date
2012-09-30
Status
Closed
Released Data Link
Team
Principal Investigator
Team Members
Related Publications
Bacon DH, BM Sass, M Bhargava, J Sminchak, and N Gupta. 2009. “Reactive Transport Modeling of CO2 and SO2 Injection into Deep Saline Formations and Their Effect on the Hydraulic Properties of Host Rocks.” In Energy Procedia: GHGT-9: 9th International Conference on Greenhouse Gas Technologies, vol. 1, no. 1, pp. 3283-3290.
Bacon DH, JR Sminchak, JL Gerst, and N Gupta. 2009b. “Validation of CO2 Injection Simulations with Monitoring Well Data.” In Energy Procedia: Proceedings of the 9th International Conference on Greenhouse Gas Technologies (GHGT-9), vol. 1, no. 1, pp. 1815-1822 .
LI W, G Lin, and D Zhang. 2014. "An Adaptive ANOVA-based PCKF for High-Dimensional Nonlinear Inverse Modeling." Journal of Computational Physics 258:752-772. doi:10.1016/j.jcp.2013.11.019
Mei D, and G Lin. 2011. "Effects of heat and mass transfer on the kinetics of CO oxidation over RuO2(110) catalyst." Catalysis Today 165(1):56-63.
Yabusaki SB, Y Fang, KH Williams, CJ Murray, AL Ward, R Dayvault, SR Waichler, DR Newcomer, FA Spane, and PE Long. 2011. "Variably Saturated Flow and Multicomponent Biogeochemical Reactive Transport Modeling of a Uranium Bioremediation Field Experiment." Journal of Contaminant Hydrology 126(3-4):271-290. doi:10.1016/j.jconhyd.2011.09.002