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Plenary

SC Conference - Activity Details



Computational Frameworks for Subsurface Energy and Environmental Modeling and Simulation

Speaker:
Mary Wheeler  (University of Texas at Austin)
Invited Speakers Session
Thursday,  09:15AM - 10:00AM
Room Ballroom D
Abstract:
Over the past sixty years modeling and simulation have been essential to the success of the petroleum industry. This in fact dates back to 1948 when von Neumann was a consultant for Humble Research in Houston, Texas. Exploration and production in deep Gulf of Mexico and the North Slope of Alaska and the design and construction of the Aleyeska pipeline could not have been achieved without modeling of coupled nonlinear partial differential equations. Today energy-related industries are facing new challenges: unprecedented demand for energy as well as growing environmental concerns over global warming and green-house gases To resolve complex scientific issues in addressing next generation energies, multidisciplinary teams of geologists, biologists, chemical, mechanical and petroleum engineers, mathematicians and computational scientists working closely together are required. Simulation needs include (1)the development of novel multiscale (molecular to field scale) and multiphysics discretizations for estimating physical characteristics and statistics of stochastic systems (2) modeling of multiscale stochastic problems for quantifying uncertainty to heterogeneity and small-scale uncertainty in subdomain system parameters (3) verification and validation of models through experimentation and simulation (4) robust optimization and optimal control for monitoring and controlling large systems (5) petascale computing on heterogeneous platforms that includes interactive visualization and seamless data management. In order to address these challenges a robust reservoir simulator comprised of coupled programs that together account for multicomponent, multiscale, multiphase (full compositional) flows and transport through porous media and through wells and that incorporate uncertainty and include robust solvers is required. The coupled programs must be able to treat different physical processes occurring simultaneously in different parts of the domain, and for computational accuracy and efficiency, should also accommodate multiple numerical schemes. In addition, these problem solving environments or frameworks must have parameter estimation and optimal control capabilities. We present a "wish list" for simulator capabilities as well as describe the methodology and parallel algorithms employed in the IPARS software being developed at The University of Texas at Austin.
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