Development of ensemble smoother–neural network and its application to history matching of channelized reservoirs

Sungil Kim, Kyungbook Lee, Jungtek Lim, Hoonyoung Jeong, Baehyun Min

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

This study develops ensemble smoother–neural network (ES-NN) that combines an ensemble smoother (ES) with a convolutional autoencoder (CAE) to yield comparable performance at a lower computational cost to that of an ensemble smoother–multiple data assimilation (ES-MDA). The ES-NN updates reservoir facies models using CAE trained by importing initial and updated ensembles of ES as input and output of the CAE, respectively, which aims to learn the principle of assimilation of the ES. The trained CAE is recurrently applied in reservoir model calibration without additional forward simulation. The ES-NN yields satisfactory history matching results in terms of production profiles and facies distributions compared to ES and ES-MDA in two case studies. This comparison highlights the efficacy of ES-NN as a prospective data assimilation tool for history matching.

Original languageEnglish
Article number107159
JournalJournal of Petroleum Science and Engineering
Volume191
DOIs
StatePublished - Aug 2020

Keywords

  • Convolutional autoencoder
  • Ensemble smoother–multiple data assimilation
  • Ensemble smoother–neural network
  • History matching

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