Application of Grid-enabled technologies for solving optimization problems in data-driven reservoir studies

被引：19
|
作者：
Parashar, M [1 ]
Klie, H
Catalyurek, U
Kurc, T
Bangerth, W
Matossian, V
Saltz, J
Wheeler, MF
机构：
[1] Rutgers State Univ, Dept Elect & Comp Engn, TASSL, Piscataway, NJ 08855 USA
[2] Univ Texas, CSM, ICES, Austin, TX 78712 USA
[3] Univ Texas, Inst Geophys, Austin, TX 78712 USA
[4] Ohio State Univ, Dept Biomed Informat, Columbus, OH 43210 USA
来源：
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2005年 / 21卷 / 01期
基金：
美国国家科学基金会;
关键词：
grid computing; optimization algorithms; distributed database;
D O I：
10.1016/j.future.2004.09.028
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper presents the use of numerical simulations coupled with optimization techniques in oil reservoir modeling and production optimization. We describe three main components of an autonomic oil production management framework. The framework implements a dynamic, data-driven approach and enables Grid-based large scale optimization formulations in reservoir modeling. (C) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码：19 / 26
页数：8
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