A Hierarchical Multigrid Method for Oil Production Optimization

Research output: Contribution to journalJournal articleResearchpeer-review

77 Downloads (Pure)

Abstract

The large-scale optimization problems that arise from oil production optimization under geological uncertainty of industry-scale reservoir models poses a challenge even for modern computer architecture. In combination with ensemble-based methods for production optimization under uncertainty, gradient-based optimization algorithms provides a powerful approach that ensures a high convergence rate. However, the spatial resolution and complexity of typical industry-scale models has a significant computational impact that renders the optimization problem intractable. To reduce the computational burden model reduction is essential. In this paper, we introduce a grid coarsening method that maintains the overall dynamics of the flow, by preserving the geological features of the model. Furthermore, we present a hierarchical multigrid method for oil production optimization. The method utilizes a hierarchy of coarse level models based on the high-fidelity model. We present the workflow of the hierarchical multigrid optimization procedure and a numerical example that demonstrates the application of oil production optimization on a synthetic reservoir. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Original languageEnglish
JournalIFAC-PapersOnLine
Volume52
Issue number1
Pages (from-to)492-497
ISSN2405-8963
DOIs
Publication statusPublished - 2019
Event12th IFAC Symposium on Dynamics and Control of Process Systems - Jurerê Beach Village Hotel, Florianópolis , Brazil
Duration: 23 Apr 201926 Apr 2019
Conference number: 12
https://dycopscab2019.sites.ufsc.br/

Conference

Conference12th IFAC Symposium on Dynamics and Control of Process Systems
Number12
LocationJurerê Beach Village Hotel
CountryBrazil
CityFlorianópolis
Period23/04/201926/04/2019
Internet address

Keywords

  • Production optimization
  • Model reduction
  • Reservoir management
  • Work-flow
  • Gradient based optimization

Cite this

@article{f41b2b019be24981bee25b932400f229,
title = "A Hierarchical Multigrid Method for Oil Production Optimization",
abstract = "The large-scale optimization problems that arise from oil production optimization under geological uncertainty of industry-scale reservoir models poses a challenge even for modern computer architecture. In combination with ensemble-based methods for production optimization under uncertainty, gradient-based optimization algorithms provides a powerful approach that ensures a high convergence rate. However, the spatial resolution and complexity of typical industry-scale models has a significant computational impact that renders the optimization problem intractable. To reduce the computational burden model reduction is essential. In this paper, we introduce a grid coarsening method that maintains the overall dynamics of the flow, by preserving the geological features of the model. Furthermore, we present a hierarchical multigrid method for oil production optimization. The method utilizes a hierarchy of coarse level models based on the high-fidelity model. We present the workflow of the hierarchical multigrid optimization procedure and a numerical example that demonstrates the application of oil production optimization on a synthetic reservoir. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.",
keywords = "Production optimization, Model reduction, Reservoir management, Work-flow, Gradient based optimization",
author = "Steen H{\o}rsholt and Hamid Nick and J{\o}rgensen, {John Bagterp}",
year = "2019",
doi = "10.1016/j.ifacol.2019.06.110",
language = "English",
volume = "52",
pages = "492--497",
journal = "IFAC-PapersOnLine",
issn = "2405-8963",
publisher = "Elsevier",
number = "1",

}

A Hierarchical Multigrid Method for Oil Production Optimization. / Hørsholt, Steen; Nick, Hamid; Jørgensen, John Bagterp.

In: IFAC-PapersOnLine, Vol. 52, No. 1, 2019, p. 492-497.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - A Hierarchical Multigrid Method for Oil Production Optimization

AU - Hørsholt, Steen

AU - Nick, Hamid

AU - Jørgensen, John Bagterp

PY - 2019

Y1 - 2019

N2 - The large-scale optimization problems that arise from oil production optimization under geological uncertainty of industry-scale reservoir models poses a challenge even for modern computer architecture. In combination with ensemble-based methods for production optimization under uncertainty, gradient-based optimization algorithms provides a powerful approach that ensures a high convergence rate. However, the spatial resolution and complexity of typical industry-scale models has a significant computational impact that renders the optimization problem intractable. To reduce the computational burden model reduction is essential. In this paper, we introduce a grid coarsening method that maintains the overall dynamics of the flow, by preserving the geological features of the model. Furthermore, we present a hierarchical multigrid method for oil production optimization. The method utilizes a hierarchy of coarse level models based on the high-fidelity model. We present the workflow of the hierarchical multigrid optimization procedure and a numerical example that demonstrates the application of oil production optimization on a synthetic reservoir. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

AB - The large-scale optimization problems that arise from oil production optimization under geological uncertainty of industry-scale reservoir models poses a challenge even for modern computer architecture. In combination with ensemble-based methods for production optimization under uncertainty, gradient-based optimization algorithms provides a powerful approach that ensures a high convergence rate. However, the spatial resolution and complexity of typical industry-scale models has a significant computational impact that renders the optimization problem intractable. To reduce the computational burden model reduction is essential. In this paper, we introduce a grid coarsening method that maintains the overall dynamics of the flow, by preserving the geological features of the model. Furthermore, we present a hierarchical multigrid method for oil production optimization. The method utilizes a hierarchy of coarse level models based on the high-fidelity model. We present the workflow of the hierarchical multigrid optimization procedure and a numerical example that demonstrates the application of oil production optimization on a synthetic reservoir. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

KW - Production optimization

KW - Model reduction

KW - Reservoir management

KW - Work-flow

KW - Gradient based optimization

U2 - 10.1016/j.ifacol.2019.06.110

DO - 10.1016/j.ifacol.2019.06.110

M3 - Journal article

VL - 52

SP - 492

EP - 497

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8963

IS - 1

ER -