Many-objective optimization algorithm applied to history matching

Junko Jhonson Juntianus Hutahaean, Vasily Demyanov, Michael Andrew Christie

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Abstract

Reservoir model calibration, called history matching in the petroleum industry, is an important task to make more accurate predictions for better reservoir management. Providing an ensemble of good matched reservoir models from history matching is essential to reproduce the observed production data from a field and to forecast reservoir performance. The nature of history matching is multi-objective because there are multiple match criteria or misfit from different production data, wells and regions in the field. In many cases, these criteria are conflicting and can be handled by the multi-objective approach. Moreover, multi-objective provides faster misfit convergence and more robust towards stochastic nature of optimization algorithms. However, reservoir history matching may feature far too many objectives that can be efficiently handled by conventional multi-objective algorithms, such as multi-objective particle swarm optimizer (MOPSO) and non-dominated sorting genetic algorithm II (NSGA II). Under an increasing number of objectives, the performance of multi-objective history matching by these algorithms deteriorates (lower match quality and slower misfit convergence). In this work, we introduce a recently proposed algorithm for many-objective optimization problem, known as reference vector-guided evolutionary algorithm (RVEA), to history matching. We apply the algorithm to history matching a synthetic reservoir model and a real field case study with more than three objectives. The paper demonstrates the superiority of the proposed RVEA to the state of the art multi-objective history matching algorithms, namely MOPSO and NSGA II.
Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence (SSCI)
PublisherIEEE
ISBN (Electronic)978-1-5090-4240-1
ISBN (Print)978-1-5090-4241-8
DOIs
Publication statusPublished - 13 Feb 2017

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history
genetic algorithm
sorting
calibration
well
prediction
particle

Keywords

  • many objectives
  • history matching
  • reservoir modelling

Cite this

Hutahaean, J. J. J., Demyanov, V., & Christie, M. A. (2017). Many-objective optimization algorithm applied to history matching. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI) IEEE. https://doi.org/10.1109/SSCI.2016.7850215
Hutahaean, Junko Jhonson Juntianus ; Demyanov, Vasily ; Christie, Michael Andrew. / Many-objective optimization algorithm applied to history matching. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017.
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Hutahaean, JJJ, Demyanov, V & Christie, MA 2017, Many-objective optimization algorithm applied to history matching. in 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE. https://doi.org/10.1109/SSCI.2016.7850215

Many-objective optimization algorithm applied to history matching. / Hutahaean, Junko Jhonson Juntianus; Demyanov, Vasily; Christie, Michael Andrew.

2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017.

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

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N2 - Reservoir model calibration, called history matching in the petroleum industry, is an important task to make more accurate predictions for better reservoir management. Providing an ensemble of good matched reservoir models from history matching is essential to reproduce the observed production data from a field and to forecast reservoir performance. The nature of history matching is multi-objective because there are multiple match criteria or misfit from different production data, wells and regions in the field. In many cases, these criteria are conflicting and can be handled by the multi-objective approach. Moreover, multi-objective provides faster misfit convergence and more robust towards stochastic nature of optimization algorithms. However, reservoir history matching may feature far too many objectives that can be efficiently handled by conventional multi-objective algorithms, such as multi-objective particle swarm optimizer (MOPSO) and non-dominated sorting genetic algorithm II (NSGA II). Under an increasing number of objectives, the performance of multi-objective history matching by these algorithms deteriorates (lower match quality and slower misfit convergence). In this work, we introduce a recently proposed algorithm for many-objective optimization problem, known as reference vector-guided evolutionary algorithm (RVEA), to history matching. We apply the algorithm to history matching a synthetic reservoir model and a real field case study with more than three objectives. The paper demonstrates the superiority of the proposed RVEA to the state of the art multi-objective history matching algorithms, namely MOPSO and NSGA II.

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Hutahaean JJJ, Demyanov V, Christie MA. Many-objective optimization algorithm applied to history matching. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE. 2017 https://doi.org/10.1109/SSCI.2016.7850215