A Hierarchical Multigrid Method for Oil Production Optimization

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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
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

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

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