Development of a robust multi-objective history matching for reliable well-based production forecasts

Baehyun Min, Joe M. Kang, Hoyoung Lee, Suryeom Jo, Changhyup Park, Ilsik Jang

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


This article presents a dynamic reservoir characterization using a new multi-objective optimization algorithm to quantify the reservoir uncertainties in history matching. The proposed method formulated Pareto-optimality with preference-ordering to derive multiple trade-off history-matched reservoir models for probabilistic production estimation. The integration of linear programming with multi-objective genetic algorithm enhances the efficiency of a multi-directional search by prioritizing the reservoir models that satisfy the aspiration levels on the discrepancy between the observed and the calculated production data. The preference levels are automatically adjusted in correspondence to the quality of the reservoir models for facilitating the model update process during optimization. An oil-field application result indicates the method outperforms the conventional multi-objective optimization method in terms of the relative average error for the production data despite a small loss of diversity-preservation among the reservoir models.

Original languageEnglish
Pages (from-to)795-809
Number of pages15
JournalEnergy Exploration and Exploitation
Issue number6
StatePublished - 1 Nov 2016

Bibliographical note

Funding Information:
This work has been supported by Europe Seventh Framework Programme FP7 (2007–2013). Project “TheraGlio” under Grant agreement no. 602993.

Publisher Copyright:
© 2016, © The Author(s) 2016.


  • Pareto-optimality
  • Reservoir characterization
  • history matching
  • multi-objective optimization
  • preference-ordering
  • trade-off


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