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Topic Name: Computing and Monitoring System for Discovery BY UCoMS

Category: Computer science & technology

Research persons:
Dr. Gabrielle Allen (CCT/CS)

Computer Science Department & CCT

Dr. Edward Seidel (CCT/Physics)

Center for Computation and Technology (CCT

Dr. Christopher D. White (PE/CCT)

Craft & Haw

Location: Louisiana State University (LSU) ,Baton Rouge, LA 70803, United States

Details

The UCoMS project, sponsored by the Department of Energy and the Louisiana Board of Regents, is researching and developing new Grid computing and sensor network technologies for the management of energy resources. Three Louisiana Universities are collaborating in this endeavour: University of Louisiana at Lafayette (ULL), Louisiana State University (LSU), and Southern University at Baton Rouge (SUBR). At LSU, the project brings together the Center for Computation & Technology, and the Departments of Petroleum Engineering and Computer Science. UCoMS is designed to support computation-intensive, fine-grained simulations and enable a huge amount of measured data storage and real-time processing, while providing safety monitoring on the well platforms.
Reservoir simulation is widely used to plan and manage oil and gas assets, providing engineers with the information needed to understand the dynamics and structure of reservoirs, quantify the uncertainties and sensitivities inherent in the models and parameters, and to forecast and optimize production. Modern reservoir simulators involve complex models for geology, fluid dynamics, and well locations and constraints, integrated with large amounts of geoscience and engineering data.
Experimental design and response surface methodology provide mechanisms to assess uncertainty by providing inference with a number of reservoir simulations, as well as to quantify the influences on production and economic forecasts. The number of simulation runs involved in the factorial designs becomes prohibatively large as the number of factors increase and complex oil-gas systems are studied. Grid-enabled UCoMS applications for reservoir simulations will dramatically relieve the burden of the expensive and time-consuming workflows traditionally associated with challenging geosciences solutions.
UCoMS is building new drilling monitoring and optimization applications to provide a set of investigative tools operating on real time drilling performance data. The task of collecting, sorting and correlating operations and drilling data for optimization analysis and implementation in near real-time is desirable given the pace and scale of modern drilling operations. One goal is to provide real time performance analysis during drilling operations, which involves coordinating large data streams and computationally intensive calculates. Drilling applications for UCoMS aim to provide a scientific basis for assessing current operations considering all of the available data and providing data mining and visualization to facilitate drillers making continuous improvements in operations.
In UCoMS, we are using Grid computing technologies to advance reservoir simulation and drilling analysis studies, coordinating the use of large scale compute and data resources at CCT and ULL through automated and reliable workflows. This work makes use of the Grid Application Toolkit (GAT) to access Grid services from generic application codes and the GridSphere portal framework to provide customized web-based user interfaces. Using the GAT and GridSphere we are building Grid-aware toolkits, such as ResGrid, which enables scenarios for reservoir uncertainty analysis. With the ResGrid software, users can easily deploy many thousands of reservoir simulations across different dynamically discovered computational resources, automatically preparing the different parameter sweeps and run configurations, and collecting and archiving results in a searchable data archive.

Publications

Zhou Lei, Dayong Huang, Archit Kulshrestha, Santiago Pena, Gabrielle Allen, Xin Li, Richard Duff, Subhash Kalla, Chris D. White, John R. Smith. Leveraging Grid Technologies For Reservoir Uncertainty Analysis. High Performance Computing Symposium (HPC06), Huntsville, Alabama. April 2-6, 2006.
Zhou Lei, Dayong Huang, Archit Kulshrestha, Santiago Pena, Gabrielle Allen, Xin Li, Richard Duff, Subhash Kalla, Chris D. White, John R. Smith. ResGrid: A Grid-aware Toolkit For Reservoir Uncertainty Analysis. IEEE International Symposium on Cluster Computing and the Grid (CCGrid06), Singapore. May 16-19, 2006