Data Driven Simulation of the Subsurface: Optimization and Uncertainty

Data Driven Simulation of the Subsurface: Optimization and Uncertainty EstimationParticipants

CSM: Mary F. Wheeler (PI), Hector Klie, Clint Dawson, Wolfgang Bangerth, Raul Tempone, Burak Aksoylu, Xiuli Gai.

PE & G: Carlos Torres-Verdin

RUTIG: Paul Stoffa (co-PI), Mrinal Sen (co-PI).

The Ohio State University: Joel Saltz (co-PI), Tahsin Kurc, Umit Catalyurek.

Rutgers University: Manish Parashar (co-PI).


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Movies (.swf)



optim_time 4.6 Mbmovie_surf 3.4 Mbmovie_contours 1 Mb

Optimizing oil production on the Grid

“Closing the loop” with optimization




Project Summary

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 have become a recognized diagnostic tool for assisting management decision. 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 effective 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 phenomenal 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 proposal include:

  • (1) development of the next generation of accurate, multi-scale, coupled chemical, fluid, geomechanical, and geophysical simulations 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 of 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).

The framework will be built upon the experience of the team in developing prototype simulators and data management tools under an on-going ITR project. The team will have access to two large dynamic datasets: one from the Gilt Edge Mine Superfund site, managed by EPA and the Idaho national Engineering and Environmental Laboratory (INEEL); and the second from an instrumented oilfield of the coast of Norway. These observational data will provide a near continuous flow of remote sensor data over time that will serve as the basis for developing and deploying the mathematical and computational tools proposed in DDSSF.

Broader Impacts

Simulation of the coupled chemical, geomechanical and geophysical response of subsurface systems is an imperative for the scientific and engineering community to understand the environmental an economic effects of human activities. Immediate impact to DOE and EPA will occur through our collaboration with INEEL. The scientific, technological and educational impact of the proposed research on dynamic data analysis will extend well beyond subsurface modeling. Our tools will have immediate applications to global warming (e.g. CO2 sequestration), national security (e.g. mines and tunnels) and the biomedical sciences (e.g. blood flow, tissue engineering, analysis of radiology and microscopy imaging data).

The proposed research activity will involve the training of undergraduates, graduate students and post-doctoral fellows at The University of Texas at Austin, Ohio State University and Rutgers University in a truly cross-disciplinary subject that has far-reaching implications. The research activity also includes outreach to industry, government laboratories, K-12 students and teachers, and undergraduates in the broader community through workshops, seminars, internships, and summer programs and curricula development. Specific outreach opportunities include the industrial affiliates program at UT Austin, collaboration between Ohio State and the Capital University Computational Science Center, the Ohio Supercomputer Center Young Women’s Summer Institute and the Rutgers Governor’s School.

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