Towards Dynamic Data-Driven Optimization of Oil Well Placement

  • Manish Parashar
  • Vincent Matossian
  • Wolfgang Bangerth
  • Hector Klie
  • Benjamin Rutt
  • Tahsin Kurc
  • Umit Catalyurek
  • Joel Saltz
  • Mary F. Wheeler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3515)


The adequate location of wells in oil and environmental applications has a significant economical impact on reservoir management. However, the determination of optimal well locations is both challenging and computationally expensive. The overall goal of this research is to use the emerging Grid infrastructure to realize an autonomic dynamic data-driven self-optimizing reservoir framework. In this paper, we present the use of distributed data to dynamically drive the optimization of well placement in an oil reservoir.


Reservoir Simulator Grid Service Reservoir Management Significant Economical Impact Open Grid Service Architecture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Manish Parashar
    • 1
  • Vincent Matossian
    • 1
  • Wolfgang Bangerth
    • 2
    • 4
  • Hector Klie
    • 2
  • Benjamin Rutt
    • 3
  • Tahsin Kurc
    • 3
  • Umit Catalyurek
    • 3
  • Joel Saltz
    • 3
  • Mary F. Wheeler
    • 2
  1. 1.TASSL, Dept. of Electrical & Computer EngineeringThe State University of New JerseyRutgersUSA
  2. 2.CSM, ICESThe University of Texas at AustinUSA
  3. 3.Dept. of Biomedical InformaticsThe Ohio State UniversityUSA
  4. 4.Institute for GeophysicsThe University of Texas at AustinUSA

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