An autonomic reservoir framework for the stochastic optimization of well placement

Wolfgang Bangerth; Hector Klie; Vincent Matossian; Manish Parashar; Mary Fanett Wheeler

doi:10.1007/s10586-005-4093-3

An autonomic reservoir framework for the stochastic optimization of well placement

Wolfgang Bangerth, Hector Klie, Vincent Matossian, Manish Parashar, Mary Fanett Wheeler

Research output: Contribution to journal › Article › peer-review

37 Scopus citations

Abstract

The adequate location of wells in oil and environmental applications has a significant economic 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 self-optimizing reservoir framework. In this paper, we present a policy-driven peer-to-peer Grid middleware substrate to enable the use of the Simultaneous Perturbation Stochastic Approximation (SPSA) optimization algorithm, coupled with the Integrated Parallel Accurate Reservoir Simulator (IPARS) and an economic model to find the optimal solution for the well placement problem.

Original language	English (US)
Pages (from-to)	255-269
Number of pages	15
Journal	Cluster Computing
Volume	8
Issue number	4
DOIs	https://doi.org/10.1007/s10586-005-4093-3
State	Published - Oct 2005

All Science Journal Classification (ASJC) codes

Software
Computer Networks and Communications

Keywords

Autonomic Grid middleware
Grid computing
Optimal well placement
Reservoir management
Stochastic optimization

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10.1007/s10586-005-4093-3

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title = "An autonomic reservoir framework for the stochastic optimization of well placement",

abstract = "The adequate location of wells in oil and environmental applications has a significant economic 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 self-optimizing reservoir framework. In this paper, we present a policy-driven peer-to-peer Grid middleware substrate to enable the use of the Simultaneous Perturbation Stochastic Approximation (SPSA) optimization algorithm, coupled with the Integrated Parallel Accurate Reservoir Simulator (IPARS) and an economic model to find the optimal solution for the well placement problem.",

keywords = "Autonomic Grid middleware, Grid computing, Optimal well placement, Reservoir management, Stochastic optimization",

author = "Wolfgang Bangerth and Hector Klie and Vincent Matossian and Manish Parashar and Wheeler, {Mary Fanett}",

note = "Funding Information: The research presented in this paper is supported in part by the National Science Foundation (NSF) via grants numbers ACI 9984357 (CAREERS), EIA 0103674 (NGS), NSF EIA-0121523/EIA-0120934 (ITR), ANI-0335244 (NRT), CNS-0305495 (NGS) and by DOE ASCI/ASAP (Caltech) via grant number 82-1052856.",

year = "2005",

month = oct,

doi = "10.1007/s10586-005-4093-3",

language = "English (US)",

volume = "8",

pages = "255--269",

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T1 - An autonomic reservoir framework for the stochastic optimization of well placement

AU - Bangerth, Wolfgang

AU - Klie, Hector

AU - Matossian, Vincent

AU - Parashar, Manish

AU - Wheeler, Mary Fanett

N1 - Funding Information: The research presented in this paper is supported in part by the National Science Foundation (NSF) via grants numbers ACI 9984357 (CAREERS), EIA 0103674 (NGS), NSF EIA-0121523/EIA-0120934 (ITR), ANI-0335244 (NRT), CNS-0305495 (NGS) and by DOE ASCI/ASAP (Caltech) via grant number 82-1052856.

PY - 2005/10

Y1 - 2005/10

N2 - The adequate location of wells in oil and environmental applications has a significant economic 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 self-optimizing reservoir framework. In this paper, we present a policy-driven peer-to-peer Grid middleware substrate to enable the use of the Simultaneous Perturbation Stochastic Approximation (SPSA) optimization algorithm, coupled with the Integrated Parallel Accurate Reservoir Simulator (IPARS) and an economic model to find the optimal solution for the well placement problem.

AB - The adequate location of wells in oil and environmental applications has a significant economic 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 self-optimizing reservoir framework. In this paper, we present a policy-driven peer-to-peer Grid middleware substrate to enable the use of the Simultaneous Perturbation Stochastic Approximation (SPSA) optimization algorithm, coupled with the Integrated Parallel Accurate Reservoir Simulator (IPARS) and an economic model to find the optimal solution for the well placement problem.

KW - Autonomic Grid middleware

KW - Grid computing

KW - Optimal well placement

KW - Reservoir management

KW - Stochastic optimization

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SP - 255

EP - 269

JO - Cluster Computing

JF - Cluster Computing

IS - 4

ER -

An autonomic reservoir framework for the stochastic optimization of well placement

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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