Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image

Kyungbook Lee, Sungil Kim, Jonggeun Choe, Baehyun Min, Hyun Suk Lee

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Most inverse reservoir modeling techniques require many forward simulations, and the posterior models cannot preserve geological features of prior models. This study proposes an iterative static modeling approach that utilizes dynamic data for rejecting an unsuitable training image (TI) among a set of TI candidates and for synthesizing history-matched pseudo-soft data. The proposed method is applied to two cases of channelized reservoirs, which have uncertainty in channel geometry such as direction, amplitude, and width. Distance-based clustering is applied to the initial models in total to select the qualified models efficiently. The mean of the qualified models is employed as a history-matched facies probability map in the next iteration of static models. Also, the most plausible TI is determined among TI candidates by rejecting other TIs during the iteration. The posterior models of the proposed method outperform updated models of ensemble Kalman filter (EnKF) and ensemble smoother (ES) because they describe the true facies connectivity with bimodal distribution and predict oil and water production with a reasonable range of uncertainty. In terms of simulation time, it requires 30 times of forward simulation in history matching, while the EnKF and ES need 9000 times and 200 times, respectively.

Original languageEnglish
Pages (from-to)127-147
Number of pages21
JournalPetroleum Science
Volume16
Issue number1
DOIs
StatePublished - 1 Feb 2019

Keywords

  • Channelized reservoirs
  • History matching
  • History-matched facies probability map
  • Iterative static modeling
  • Multiple-point statistics
  • Training image rejection

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