Physical-stochastic (greybox) modeling of slugging

Jan Kloppenborg Møller*, Goran Goranović, Niels Kjølstad Poulsen, Henrik Madsen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

265 Downloads (Pure)

Abstract

We use state-based stochastic greybox modeling - combining physics and statistics - to model the slugging phenomenon. We extend the model of DiMeglio et al. (2010) to include random components and variable flow coefficients, providing 30 seconds prediction intervals. Altogether six models, each comprising no more than ten equations, are fitted to off-shore riser training data and then cross-validated on new data sets. We use advanced statistical methods to 1) obtain optimal parameters of a given model fitted to measurements, 2) give model predictions with uncertainty intervals, and 3) quantitatively measure the relative goodness of the extended models. These features of our reductive method are general and can be applied to any data sets. For the slugging data, simpler models are preferable over the more complex ones (although the differences are minute for practical purposes in oil and gas industry) and a high statistical significance obtained on the training data does not imply improved long term prediction on independent data. Better physical (mechanistic) models to capture slugging oscillations are needed, ultimately to develop effective control strategies.
Original languageEnglish
Book seriesI F A C Workshop Series
Volume51
Issue number8
Pages (from-to)197-202
ISSN1474-6670
DOIs
Publication statusPublished - 2018

Keywords

  • Stochastic greybox modeling
  • Slugging

Fingerprint

Dive into the research topics of 'Physical-stochastic (greybox) modeling of slugging'. Together they form a unique fingerprint.

Cite this