Review of nonlinear kalman, ensemble and particle filtering with application to the reservoir history matching problem

Xiaodong Luo*, Ibrahim Hoteit, Lian Duan, Wenhui Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

5 Scopus citations

Abstract

This chapter reviews the recent advances in Bayesian filtering approaches, with the focus on those suitable for data assimilation in high-dimensional systems. We discuss the similarities and differences of these filtering approaches, and compare their performance in an application to the history matching problem in a synthetic, twodimensional, oil-water reservoir model.

Original languageEnglish (US)
Title of host publicationNonlinear Estimation and Applications to Industrial Systems Control
PublisherNova Science Publishers, Inc.
Pages197-224
Number of pages28
ISBN (Print)9781619428980
StatePublished - Nov 1 2012

Keywords

  • Data assimilation
  • Ensemble Kalman filter
  • Gaussian sum filter
  • History matching in reservoir models
  • Nonlinear Kalman filter
  • Particle filter
  • Sequential Bayesian filtering

ASJC Scopus subject areas

  • Social Sciences(all)

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