Award Abstract #0426241
ITR: Collaborative Research (ASE+EVS)-(dmc+sim): Data Driven Simulation of the Subsurface: Optimization and Uncertainty Estimation

NSF Org:	CNS Division of Computer and Network Systems
Initial Amendment Date:	September 24, 2004
Latest Amendment Date:	September 24, 2004
Award Number:	0426241
Award Instrument:	Standard Grant
Program Manager:	Anita J. LaSalle CNS Division of Computer and Network Systems CSE Directorate for Computer & Information Science & Engineering
Start Date:	October 1, 2004
Expires:	September 30, 2008 (Estimated)
Awarded Amount to Date:	$220000
Investigator(s):	Joel Saltz jhsaltz@emory.edu (Principal Investigator) Umit Catalyurek (Co-Principal Investigator) Tahsin Kurc (Co-Principal Investigator)
Sponsor:	Ohio State University Research Foundation 1960 KENNY RD Columbus, OH 43210 614/292-3732
NSF Program(s):	ITR FOR NATIONAL PRIORITIES
Field Application(s):	0000912 Computer Science
Program Reference Code(s):	HPCC, 9218
Program Element Code(s):	7314

ABSTRACT

Intellectual Merit. Remote sensing is employed in science and engineering problems to infer material properties when these properties can not be directly sampled. To better understand and manage our environment for safety and economic reasons, much progress has been made in imaging the subsurface and estimating physical properties based on remote sensing data. Repeated observations over targets for environmental remediation and reservoir production has become a recognized diagnostic tool for assisting management decisions. In addition, improved optimization techniques capable of responding to large, multi-resolution, disparate, dynamic datasets in a fault tolerant and adaptive fashion are a fundamental requirement for effectively estimating and minimizing the uncertainty in any data driven application. The integrated and e_ective treatment of these issues motivates the present project. The assembled research team proposes to advance the mathematical, engineering and computational foundations necessary to enhance our understanding and extend the predictive capabilities of the physical processes that govern the subsurface phenomena at multiple temporal and spatial scales. Target applications include management of aquifers for water resources, optimizing oil and gas production, and monitoring environmental risks e.g., at waste containment sites or arising from natural hazards.

The intellectual merits of the project include: (1) development of the next generation of accurate, multi-scale, coupled chemical, uid, geomechanical, and geophysical simulators for modeling instrumented subsurface environments; (2) large scale optimization techniques (based on a hybridization of global and local approaches) to drive reliable decision-making and a dynamic symbiotic feedback between computation and data; (3) deployment of an autonomic Grid middleware for providing the adequate processing substrate and data management services for (1) and (2). The realization of the above contributions will result in the Data Driven Subsurface Simulation Framework (DDSSF).

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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