TY - JOUR
T1 - Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image
AU - Lee, Kyungbook
AU - Kim, Sungil
AU - Choe, Jonggeun
AU - Min, Baehyun
AU - Lee, Hyun Suk
N1 - Funding Information:
Acknowledgements This work was supported by Korea Institute of Geoscience and Mineral Resources (Project No. GP2017-024) and Ministry of Trade and Industry [Project No. NP2017-021 (20172510102090)]. B. Min was funded by National Research Foundation of Korea (NRF) Grants (Nos. NRF-2017R1C1B5017767, NRF-2017K2A9A1A01092734). The authors also thank the Institute of Engineering Research at Seoul National University, Korea.
Funding Information:
This work was supported by Korea Institute of Geoscience and Mineral Resources (Project No. GP2017-024) and Ministry of Trade and Industry [Project No. NP2017-021 (20172510102090)]. B. Min was funded by National Research Foundation of Korea (NRF) Grants (Nos. NRF-2017R1C1B5017767, NRF-2017K2A9A1A01092734). The authors also thank the Institute of Engineering Research at Seoul National University, Korea.
Publisher Copyright:
© 2018, The Author(s).
PY - 2019/2/1
Y1 - 2019/2/1
N2 - 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.
AB - 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.
KW - Channelized reservoirs
KW - History matching
KW - History-matched facies probability map
KW - Iterative static modeling
KW - Multiple-point statistics
KW - Training image rejection
UR - http://www.scopus.com/inward/record.url?scp=85052701431&partnerID=8YFLogxK
U2 - 10.1007/s12182-018-0254-x
DO - 10.1007/s12182-018-0254-x
M3 - Article
AN - SCOPUS:85052701431
SN - 1672-5107
VL - 16
SP - 127
EP - 147
JO - Petroleum Science
JF - Petroleum Science
IS - 1
ER -