Principle component analysis methods for covariance localization in the ensemble kalman filter history matching

Junzhe Jiang, Sheldon Gorell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

History matching is a widely used reservoir simulation workflow. Its goal is to create models which reasonably match historical field injection and production data so future predictions can be made. Many methods have been developed in the past to try to solve this problem. One set of methods that have been those that involve ensemble data assimilation. An example is the Ensemble Kalman Filter (EnKF), which has been widely implemented. A key issue with ensemble methods is that Under sampling can severely degrade the reliability of the estimation. In this paper we introduce a new method to improve the result quality in ensemble data assimilation methods.

Original languageEnglish
Title of host publicationSociety of Petroleum Engineers - SPE Western Regional Meeting 2018
PublisherSociety of Petroleum Engineers (SPE)
ISBN (Electronic)9781510862425
DOIs
StatePublished - 2018
EventSPE Western Regional Meeting 2018 - Garden Grove, United States
Duration: Apr 22 2018Apr 26 2018

Publication series

NameSPE Western Regional Meeting Proceedings
Volume2018-April

Conference

ConferenceSPE Western Regional Meeting 2018
CountryUnited States
CityGarden Grove
Period04/22/1804/26/18

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    Jiang, J., & Gorell, S. (2018). Principle component analysis methods for covariance localization in the ensemble kalman filter history matching. In Society of Petroleum Engineers - SPE Western Regional Meeting 2018 (SPE Western Regional Meeting Proceedings; Vol. 2018-April). Society of Petroleum Engineers (SPE). https://doi.org/10.2118/190015-ms