Hybrid ensemble kalman filter With coarse-scale constraint for nonlinear dynamics

Shingo Watanabe*, Akhil Datta-Gupta, Yalchin Efendiev, Deepak Devegowda

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Interest in ensemble Kalman filters (EnKFs) is driven by the need for continuous reservoir-model updating and uncertainty assessments based on dynamic data. The EnKF approach relies on sample-based statistics derived from an ensemble of reservoir- model realizations. Sampling error in these statistics, particularly with the use of modest ensemble sizes, can degrade EnKF performance severely, leading to parameter overshoots and filter divergence. The proposed hybrid-multiscale EnKF improved operational-data assimilation and helped overcome many limitations associated with the classical EnKF implementation.

Original languageEnglish (US)
Pages (from-to)83-85
Number of pages3
JournalJPT, Journal of Petroleum Technology
Volume62
Issue number4
StatePublished - Apr 1 2010

ASJC Scopus subject areas

  • Fuel Technology
  • Industrial relations
  • Energy Engineering and Power Technology
  • Strategy and Management

Fingerprint Dive into the research topics of 'Hybrid ensemble kalman filter With coarse-scale constraint for nonlinear dynamics'. Together they form a unique fingerprint.

Cite this