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

E Insuasty, Paul van den Hof, S Weiland, Jan Dirk Jansen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)
11 Downloads (Pure)


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 publicationSPE Reservoir Simulation Conference
Subtitle of host publicationMontgomery, Texas, USA
Number of pages18
Publication statusPublished - 2017
EventSPE Reservoir Simulation Conference - Conference Center at La Torretta, Montgomery, United States
Duration: 20 Feb 201722 Feb 2017
Conference number: 23


ConferenceSPE Reservoir Simulation Conference
Abbreviated titleRSC 2017
CountryUnited States
Internet address

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