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Four-Dimensional History Matching Using ES-MDA and Flow-Based Distance-to-Front Measurement

Author

Listed:
  • Eduardo Barrela

    (TotalEnergies S.E.—Centre Scientifique & Technique Jean Féger, Av. Larribau, 64000 Pau, France)

  • Philippe Berthet

    (TotalEnergies S.E.—Centre Scientifique & Technique Jean Féger, Av. Larribau, 64000 Pau, France)

  • Mario Trani

    (TotalEnergies S.E.—Centre Scientifique & Technique Jean Féger, Av. Larribau, 64000 Pau, France)

  • Olivier Thual

    (Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique, 42 Av. Gaspard Coriolis, 31100 Toulouse, France)

  • Corentin Lapeyre

    (Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique, 42 Av. Gaspard Coriolis, 31100 Toulouse, France)

Abstract

The use of 4D seismic data in history matching has been a topic of great interest in the hydrocarbon industry as it can provide important information regarding changes in subsurfaces caused by fluid substitution and other factors where well data is not available. However, the high dimensionality and uncertainty associated with seismic data make its integration into the history-matching process a challenging task. Methods for adequate data reduction have been proposed in the past, but most address 4D information mismatch from a purely mathematical or image distance-based standpoint. In this study, we propose a quantitative and flow-based approach for integrating 4D seismic data into the history-matching process. By introducing a novel distance parametrization technique for measuring front mismatch information using streamlines, we address the problem from a flow-based standpoint; at the same time, we maintain the amount of necessary front data at a reduced and manageable amount. The proposed method is tested, and its results are compared on a synthetic case against another traditional method based on the Hausdorff distance. The effectiveness of the method is also demonstrated on a semi-synthetic model based on a real-case scenario, where the standard Hausdorff methodology could not be applied due to high data dimensionality.

Suggested Citation

  • Eduardo Barrela & Philippe Berthet & Mario Trani & Olivier Thual & Corentin Lapeyre, 2023. "Four-Dimensional History Matching Using ES-MDA and Flow-Based Distance-to-Front Measurement," Energies, MDPI, vol. 16(24), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:7984-:d:1296995
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    References listed on IDEAS

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    1. Xiaodong Luo & Tuhin Bhakta & Morten Jakobsen & Geir Nævdal, 2018. "Efficient big data assimilation through sparse representation: A 3D benchmark case study in petroleum engineering," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-32, July.
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