Cloud-enabled Subsurface Modeling

Project Information

Discipline
Petroleum Engineering (113) 
Subdiscipline
11.07 Computer Science 
Orientation
Education 
Abstract

Cloud computing has a significant promise in hydrocarbon reservoir modeling and exploration studies. Reservoir modeling involves sparse data and therefore, multiple realizations, stochastic models are necessary to reduce uncertainty in reservoir characterization and management. Localized cluster computing environments can harness the scalability of reservoir simulations and reduce the simulation runtime. However, they are not growing as fast as the amount of work and data demands in this application area. As a result, cloud computing is becoming an emerging computing standard and the main purpose this project is to deploy a cloud based solutions for reservoir modeling applications and evaluate various standards and APIs for their interoperability, and areas for future development. 

Intellectual Merit

Harnessing the scalability reservoir modeling and simulations, the intellectual merit of this project is to (1) evaluate the interoperability of cloud standards applicable to this application area, and (2) facilitates the comparison of performance characteristics between local cluster, grid, and cloud computing environments.

Broader Impacts

The effort could be used to demonstrate the scope of advanced distributed computing in petroleum engineering. A portion of this work will be used to develop/demonstrate educational content for the future engineering workforce in petroleum engineering, computer science and high performance computing.

Project Contact

Project Lead
Ravi Vadapalli (rvadapalli) 
Project Manager
Ravi Vadapalli (rvadapalli) 
Project Members
Akash Pargat, Alan Sill  

Resource Requirements

Hardware Systems
  • alamo (Dell optiplex at TACC)
  • foxtrot (IBM iDataPlex at UF)
  • hotel (IBM iDataPlex at U Chicago)
  • india (IBM iDataPlex at IU)
  • sierra (IBM iDataPlex at SDSC)
  • xray (Cray XM5 at IU)
  • bravo (large memory machine at IU)
  • delta (GPU Cloud)
  • Not sure
 
Use of FutureGrid

The FutureGrid resources will be used to evaluate and develop APIs.

Scale of Use

Initially the resources will be used to test existing capabilities with FutureGrid and their interoperability with reservoir modeling applications. During the course, simulation models will be developed and at that time the usage may increase. Until then, the scale of usage is minimal.

Project Timeline

Submitted
11/07/2012 - 17:01 
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