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

Title
Development of ensemble smoother-neural network and its application to history matching of channelized reservoirs
Authors
Kim, SungilLee, KyungbookLim, JungtekJeong, HoonyoungMin, Baehyun
Ewha Authors
민배현
Issue Date
2020
Journal Title
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
ISSN
0920-4105JCR Link

1873-4715JCR Link
Citation
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING vol. 191
Keywords
Ensemble smoother-neural networkConvolutional autoencoderEnsemble smoother-multiple data assimilationHistory matching
Publisher
ELSEVIER
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
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.
DOI
10.1016/j.petrol.2020.107159
Appears in Collections:
엘텍공과대학 > 기후·에너지시스템공학전공 > Journal papers
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