## 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/Territory | United States |

City | Montgomery |

Period | 20/02/17 → 22/02/17 |