Utilizing grid computing technologies for advanced reservoir studies

Zhou Lei, Gabrielle Allen, Dayong Huang, Hartmut Kaiser, Xin Li, Chris White

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Reservoir studies are crucial to obtain accurate assessments and predictions of reservoir performance. However, this is a challenging issue because 1) it relies on massive modeling-related, geographically distributed, terabyte or even petabyte sized datasets (seismic and well-logging data), 2) needs to rapidly perform hundreds or thousands of simulations, being identical runs with different reservoir models circulating the impacts of various uncertainty factors, 3) the lack of easy-to-use problem solving toolkits to assist the uncertainty analysis.The poster focuses on leveraging Grid computing technologies to address the challenging issue mentioned above. It describes a newly developed data archive tool based on metadata and replica services and high performance file transfer. Our task farming framework enables a large amount of parallel job runs across a Grid, a related Grid portal eases the management of advanced reservoir studies. Our solutions are being employed by other Grid applications.

Original languageEnglish (US)
Title of host publicationProceedings of the 2006 ACM/IEEE Conference on Supercomputing, SC'06
DOIs
StatePublished - 2006
Externally publishedYes

Publication series

NameProceedings of the 2006 ACM/IEEE Conference on Supercomputing, SC'06

ASJC Scopus subject areas

  • Computer Science(all)

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