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A simulation and data analysis system for large-scale, data-driven oil reservoir simulation studies

  • Tahsin Kurc
  • , Umit Catalyurek
  • , Xi Zhang
  • , Joel Saltz
  • , Ryan Martino
  • , Mary Wheeler
  • , Małgorzata Peszyńska
  • , Alan Sussman
  • , Christian Hansen
  • , Mrinal Sen
  • , Roustam Seifoullaev
  • , Paul Stoffa
  • , Carlos Torres-Verdin
  • , Manish Parashar
  • Ohio State University
  • University of Texas at Austin
  • Oregon State University
  • University of Maryland, College Park
  • Rutgers - The State University of New Jersey, New Brunswick

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

The main goal of oil reservoir management is to provide more efficient, cost-effective and environmentally safer production of oil from reservoirs. Numerical simulations can aid in the design and implementation of optimal production strategies. However, traditional simulation-based approaches to optimizing reservoir management are rapidly overwhelmed by data volume when large numbers of realizations are sought using detailed geologic descriptions. In this paper, we describe a software architecture to facilitate large-scale simulation studies, involving ensembles of long-running simulations and analysis of vast volumes of output data.

Original languageEnglish
Pages (from-to)1441-1467
Number of pages27
JournalConcurrency and Computation: Practice and Experience
Volume17
Issue number11
DOIs
StatePublished - Sep 2005

Keywords

  • Data intensive computing
  • Data management
  • Grid computing
  • Oil reservoir simulation

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