Reliable initial model selection for efficient characterization of channel reservoirs in ensemble Kalman filter

IF 2.6 3区 工程技术 Q3 ENERGY & FUELS Journal of Energy Resources Technology-transactions of The Asme Pub Date : 2023-07-11 DOI:10.1115/1.4062926
Doeon Kim, Youjun Lee, J. Choe
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Abstract

Ensemble Kalman filter is typically utilized to characterize reservoirs with high uncertainty. However, it requires a large number of reservoir models for stable and reliable update of its members, resulting in high simulation time. In this study, we propose a sampling scheme using convolutional autoencoder and principal component analysis for fast and reliable channel reservoir characterization. The proposed method provides good initial models similar to the reference model and gives successful model update for reliable quantification of future performances of channel reservoirs. Despite using fewer than 50 reservoir models, we achieve similar or even superior results compared to using all 400 initial models in this study. We demonstrate that the proposed scheme with ensemble Kalman filter provides faithful assimilation results while saving computation time.
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集成卡尔曼滤波器中河道储层有效表征的可靠初始模型选择
集合卡尔曼滤波器通常用于表征具有高不确定性的储层。然而,它需要大量的储层模型来稳定可靠地更新其成员,导致模拟时间很长。在这项研究中,我们提出了一种使用卷积自动编码器和主成分分析的采样方案,用于快速可靠的河道储层表征。所提出的方法提供了类似于参考模型的良好初始模型,并成功地对模型进行了更新,以可靠地量化河道油藏的未来性能。尽管使用了不到50个储层模型,但与本研究中使用的所有400个初始模型相比,我们获得了相似甚至更好的结果。我们证明了所提出的带有集成卡尔曼滤波器的方案在节省计算时间的同时提供了可靠的同化结果。
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来源期刊
CiteScore
6.40
自引率
30.00%
发文量
213
审稿时长
4.5 months
期刊介绍: Specific areas of importance including, but not limited to: Fundamentals of thermodynamics such as energy, entropy and exergy, laws of thermodynamics; Thermoeconomics; Alternative and renewable energy sources; Internal combustion engines; (Geo) thermal energy storage and conversion systems; Fundamental combustion of fuels; Energy resource recovery from biomass and solid wastes; Carbon capture; Land and offshore wells drilling; Production and reservoir engineering;, Economics of energy resource exploitation
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