The PI proposes to use a variety of techniques for modeling processes in porous media relevant to modeling methane evolution in subsurface. The PI will combine traditional continuum partial differential equations (PDE)-based models at mesoscale, i.e. lab or field scale, with continuum and discrete models at porescale. In particular, coarse-grained statistical mechanics models are used to develop better characterization of metastable phenomena such as adsorption and phase transitions. The different models will be connected and, to the extent possible, will exchange data dynamically and on demand. The challenges to continuum models are in their nonlinear coupled character and handling thermodynamics constraints, while the difficulties to the porescale and discrete models include their enormous computational complexity. In the project the project team shall develop ways to encapsulate microscale results in the form of 'library entries' available to continuum models by ensuring that they are meaningful in a probabilistic sense. The project involves computational studies, algorithm development, and rigorous analysis of the models and the associated error.

This investigation shall focus on two applications important for global climate and energy studies, namely, on Enhanced Coalbed Methane (ECBM) recovery, and modeling methane hydrate evolution in subsea sediments (MH). New computational models for these applications and their mathematical analyses are needed for better understanding of various associated hazards and for improved prediction of the fate of methane and its management. Primary impacts of the project are in development of new paradigm of modeling between the models in subsurface and in improved understanding of how porescale phenomena affect the processes at mesoscale. Project results will directly or indirectly aid the modeling efforts in subsurface hydrology, oil- and gas-reservoir modeling, and geological carbon storage projects. In addition, various other disciplines will benefit from the new hybrid discrete-continuum algorithms developed in this project. Other community benefits include the Porescale Benchmark project set up by the investigators, interdisciplinary training of graduate and undergraduates, and development of new senior/graduate level classes and seminars devoted to modeling of processes relevant to global energy and climate issues.

National Science Foundation (NSF)
Division of Mathematical Sciences (DMS)
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Junping Wang
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Oregon State University
United States
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