### 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 language | English |
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Title of host publication | Society of Petroleum Engineers - SPE Reservoir Simulation Conference 2017 |

Place of Publication | Richardson |

Publisher | Society of Petroleum Engineers (SPE) |

Pages | 1965-1982 |

Number of pages | 18 |

ISBN (Print) | 9781510838864 |

DOIs | |

Publication status | Published - 1 Jan 2017 |

Event | SPE Reservoir Simulation Conference 2017 - Montgomery, United States Duration: 20 Feb 2017 → 22 Feb 2017 |

### Conference

Conference | SPE Reservoir Simulation Conference 2017 |
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Country | United States |

City | Montgomery |

Period | 20/02/17 → 22/02/17 |

### Fingerprint

### Cite this

*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

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*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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Low-dimensional tensor representations for the estimation of petrophysical reservoir parameters

AU - Insuasty, E.

AU - Van den Hof, P.M.J.

AU - Weiland, S.

AU - Jansen, J.D.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85054575839&partnerID=8YFLogxK

U2 - 10.2118/182707-MS

DO - 10.2118/182707-MS

M3 - Conference contribution

AN - SCOPUS:85054575839

SN - 9781510838864

SP - 1965

EP - 1982

BT - Society of Petroleum Engineers - SPE Reservoir Simulation Conference 2017

PB - Society of Petroleum Engineers (SPE)

CY - Richardson

ER -