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

3 Citations (Scopus)

Abstract

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)
Pages1965-1982
Number of pages18
ISBN (Print)9781510838864
DOIs
Publication statusPublished - 1 Jan 2017
EventSPE Reservoir Simulation Conference 2017 - Montgomery, United States
Duration: 20 Feb 201722 Feb 2017

Conference

ConferenceSPE Reservoir Simulation Conference 2017
CountryUnited States
CityMontgomery
Period20/02/1722/02/17

Fingerprint

Tensors
Tensor
Parameterization
Permeability
Parameter estimation
Parameter Estimation
parameterization
permeability
Ensemble Kalman Filter
History Matching
Spatial Structure
Multiple Models
Kalman filter
history
Kalman filters
Principal component analysis
Principal Component Analysis
Dimensionality
principal component analysis
Update

Cite this

Insuasty, E., Van den Hof, P. M. J., Weiland, S., & Jansen, J. D. (2017). Low-dimensional tensor representations for the estimation of petrophysical reservoir parameters. In Society of Petroleum Engineers - SPE Reservoir Simulation Conference 2017 (pp. 1965-1982). Richardson: Society of Petroleum Engineers (SPE). https://doi.org/10.2118/182707-MS
Insuasty, E. ; Van den Hof, P.M.J. ; Weiland, S. ; Jansen, J.D. / Low-dimensional tensor representations for the estimation of petrophysical reservoir parameters. Society of Petroleum Engineers - SPE Reservoir Simulation Conference 2017. Richardson : Society of Petroleum Engineers (SPE), 2017. pp. 1965-1982
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Insuasty, E, Van den Hof, PMJ, Weiland, S & Jansen, JD 2017, Low-dimensional tensor representations for the estimation of petrophysical reservoir parameters. in Society of Petroleum Engineers - SPE Reservoir Simulation Conference 2017. Society of Petroleum Engineers (SPE), Richardson, pp. 1965-1982, SPE Reservoir Simulation Conference 2017, Montgomery, United States, 20/02/17. https://doi.org/10.2118/182707-MS

Low-dimensional tensor representations for the estimation of petrophysical reservoir parameters. / Insuasty, E.; Van den Hof, P.M.J.; Weiland, S.; Jansen, J.D.

Society of Petroleum Engineers - SPE Reservoir Simulation Conference 2017. Richardson : Society of Petroleum Engineers (SPE), 2017. p. 1965-1982.

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

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Insuasty E, Van den Hof PMJ, Weiland S, Jansen JD. Low-dimensional tensor representations for the estimation of petrophysical reservoir parameters. In Society of Petroleum Engineers - SPE Reservoir Simulation Conference 2017. Richardson: Society of Petroleum Engineers (SPE). 2017. p. 1965-1982 https://doi.org/10.2118/182707-MS