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History matching through dynamic decision-making

Author

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  • Cristina C B Cavalcante
  • Célio Maschio
  • Antonio Alberto Santos
  • Denis Schiozer
  • Anderson Rocha

Abstract

History matching is the process of modifying the uncertain attributes of a reservoir model to reproduce the real reservoir performance. It is a classical reservoir engineering problem and plays an important role in reservoir management since the resulting models are used to support decisions in other tasks such as economic analysis and production strategy. This work introduces a dynamic decision-making optimization framework for history matching problems in which new models are generated based on, and guided by, the dynamic analysis of the data of available solutions. The optimization framework follows a ‘learning-from-data’ approach, and includes two optimizer components that use machine learning techniques, such as unsupervised learning and statistical analysis, to uncover patterns of input attributes that lead to good output responses. These patterns are used to support the decision-making process while generating new, and better, history matched solutions. The proposed framework is applied to a benchmark model (UNISIM-I-H) based on the Namorado field in Brazil. Results show the potential the dynamic decision-making optimization framework has for improving the quality of history matching solutions using a substantial smaller number of simulations when compared with a previous work on the same benchmark.

Suggested Citation

  • Cristina C B Cavalcante & Célio Maschio & Antonio Alberto Santos & Denis Schiozer & Anderson Rocha, 2017. "History matching through dynamic decision-making," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-32, June.
  • Handle: RePEc:plo:pone00:0178507
    DOI: 10.1371/journal.pone.0178507
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    Cited by:

    1. Biró, Péter & Gudmundsson, Jens, 2021. "Complexity of finding Pareto-efficient allocations of highest welfare," European Journal of Operational Research, Elsevier, vol. 291(2), pages 614-628.
    2. Rezny, Lukas & White, James Buchanan & Maresova, Petra, 2019. "The knowledge economy: Key to sustainable development?," Structural Change and Economic Dynamics, Elsevier, vol. 51(C), pages 291-300.
    3. Warnick, Benjamin J. & Kier, Alexander S. & LaFrance, Emily M. & Cuttler, Carrie, 2021. "Head in the clouds? Cannabis users' creativity in new venture ideation depends on their entrepreneurial passion and experience," Journal of Business Venturing, Elsevier, vol. 36(2).
    4. Moghaddam, Mahboobeh & Pearce, Robin H. & Mokhtar, Hamid & Prato, Carlo G., 2020. "A generalised model for container drayage operations with heterogeneous fleet, multi-container sizes and two modes of operation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 139(C).
    5. He, Yifan & Aranha, Claus & Hallam, Antony & Chassagne, Romain, 2022. "Optimization of subsurface models with multiple criteria using Lexicase Selection," Operations Research Perspectives, Elsevier, vol. 9(C).

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