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

doi:10.1007/11428848_85

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

Biomedical Informatics

Rutgers - The State University of New Jersey, New Brunswick
University of Texas at Austin
Ohio State University

Research output: Contribution to journal › Conference article › peer-review

11 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	656-663
Number of pages	8
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3515
Issue number	II
DOIs	https://doi.org/10.1007/11428848_85
State	Published - 2005
Event	5th International Conference on Computational Science - ICCS 2005 - Atlanta, GA, United States Duration: May 22 2005 → May 25 2005

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10.1007/11428848_85

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Parashar, M., Matossian, V., Bangerth, W., Klie, H., Rutt, B., Kurc, T., Catalyurek, U., Saltz, J., & Wheeler, M. F. (2005). Towards dynamic data-driven optimization of oil well placement. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3515(II), 656-663. https://doi.org/10.1007/11428848_85

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abstract = "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.",

author = "Manish Parashar and Vincent Matossian and Wolfgang Bangerth and Hector Klie and Benjamin Rutt and Tahsin Kurc and Umit Catalyurek and Joel Saltz and Wheeler, \{Mary F.\}",

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Parashar, M, Matossian, V, Bangerth, W, Klie, H, Rutt, B, Kurc, T, Catalyurek, U, Saltz, J & Wheeler, MF 2005, 'Towards dynamic data-driven optimization of oil well placement', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3515, no. II, pp. 656-663. https://doi.org/10.1007/11428848_85

Towards dynamic data-driven optimization of oil well placement. / Parashar, Manish; Matossian, Vincent; Bangerth, Wolfgang et al.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3515, No. II, 2005, p. 656-663.

Research output: Contribution to journal › Conference article › peer-review

TY - JOUR

T1 - Towards dynamic data-driven optimization of oil well placement

AU - Parashar, Manish

AU - Matossian, Vincent

AU - Bangerth, Wolfgang

AU - Klie, Hector

AU - Rutt, Benjamin

AU - Kurc, Tahsin

AU - Catalyurek, Umit

AU - Saltz, Joel

AU - Wheeler, Mary F.

PY - 2005

Y1 - 2005

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/25144447697

U2 - 10.1007/11428848_85

DO - 10.1007/11428848_85

M3 - Conference article

AN - SCOPUS:25144447697

SN - 0302-9743

VL - 3515

SP - 656

EP - 663

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

IS - II

T2 - 5th International Conference on Computational Science - ICCS 2005

Y2 - 22 May 2005 through 25 May 2005

ER -

Towards dynamic data-driven optimization of oil well placement

Abstract

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