Low-dimensional tensor representations for the estimation of petrophysical reservoir parameters

E. Insuasty, P.M.J. Van den Hof, S. Weiland, J.D. Jansen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)


In this work, the application of tensor methodologies for computer-assisted history matching of channelized reservoirs is explored. A tensor-based approach is used for the parameterization of petrophysical parameters to reduce the dimensionality of the parameter estimation problem. Building on the work of Afra and Gildin (2013); Afra et.al. (2014); Afra and Gildin (2016), permeability fields of multiple model realizations are collected in a tensor form which is subsequently decomposed to derive a low-dimensional representation of the dominant spatial structures in the models. This representation then is used to estimate an identifiable reduced set of parameters using an ensemble Kalman filter (EnKF) strategy. This approach is attractive for the parameter estimation of permeabilities because it increases the ability to represent channelized structures in the updates resulting in an improved predictive capacity of the history-matched models. In particular, channel continuity is better preserved than with a Principal Component Analysis (PCA) parameterization.

Original languageEnglish
Title of host publicationSociety of Petroleum Engineers - SPE Reservoir Simulation Conference 2017
Place of PublicationRichardson
PublisherSociety of Petroleum Engineers (SPE)
Number of pages18
ISBN (Print)9781510838864
Publication statusPublished - 1 Jan 2017
EventSPE Reservoir Simulation Conference 2017 - Montgomery, United States
Duration: 20 Feb 201722 Feb 2017


ConferenceSPE Reservoir Simulation Conference 2017
Country/TerritoryUnited States


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