TY - GEN
T1 - Application of a serial denoising autoencoder for geological plausibility of a channelized reservoir in history matching
AU - Kim, S.
AU - Min, B.
AU - Choe, J.
N1 - Funding Information:
The authors acknowledge the project of Korea Institute of Geoscience and Mineral Resources (Project No. GP2017-024) and the project of Korea Institute of Energy Technology Evaluation and Planning granted financial resources from the Ministry of Trade, Industry, and Energy, Republic of Korea (No. 20172510102090). Dr. Baehyun Min was partially supported by the National Research Foundation of Korea (No. 2018R1A6A1A08025520 and No. 2019R1C1C1002574).
Publisher Copyright:
© EAGE 2019.
PY - 2019
Y1 - 2019
N2 - Denoising autoencoder (DAE) is utilized to preserve and improve geological reality and plausibility in a channelized reservoir model during history matching by ensemble smoother with multiple data assimilation (ES-MDA). As one of history matching methods, ES-MDA calibrates reservoir properties such as rock facies corresponding to production history. While ES-MDA modifies reservoir parameters, it recognizes them only as figures not honoring to geological features. Thus, conservation of geological characteristics during calibration of reservoir parameters is challenging in ES-MDA. DAE is trained to restore lost connectivity and pattern of an original geological concept and it is applied to posterior reservoir models after an assimilation by ES-MDA. ES-MDA combined with DAE shows not only geologically enhanced channel models but also well-matched production prediction.
AB - Denoising autoencoder (DAE) is utilized to preserve and improve geological reality and plausibility in a channelized reservoir model during history matching by ensemble smoother with multiple data assimilation (ES-MDA). As one of history matching methods, ES-MDA calibrates reservoir properties such as rock facies corresponding to production history. While ES-MDA modifies reservoir parameters, it recognizes them only as figures not honoring to geological features. Thus, conservation of geological characteristics during calibration of reservoir parameters is challenging in ES-MDA. DAE is trained to restore lost connectivity and pattern of an original geological concept and it is applied to posterior reservoir models after an assimilation by ES-MDA. ES-MDA combined with DAE shows not only geologically enhanced channel models but also well-matched production prediction.
UR - http://www.scopus.com/inward/record.url?scp=85073107437&partnerID=8YFLogxK
U2 - 10.3997/2214-4609.201902194
DO - 10.3997/2214-4609.201902194
M3 - Conference contribution
AN - SCOPUS:85073107437
T3 - 4th EAGE Conference on Petroleum Geostatistics
BT - 4th EAGE Conference on Petroleum Geostatistics
PB - European Association of Geoscientists and Engineers, EAGE
Y2 - 2 September 2019 through 6 September 2019
ER -