{"title":"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","authors":"Doeon Kim, Youjun Lee, J. Choe","doi":"10.1115/1.4062926","DOIUrl":null,"url":null,"abstract":"\n 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.","PeriodicalId":15676,"journal":{"name":"Journal of Energy Resources Technology-transactions of The Asme","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reliable initial model selection for efficient characterization of channel reservoirs in ensemble Kalman filter\",\"authors\":\"Doeon Kim, Youjun Lee, J. Choe\",\"doi\":\"10.1115/1.4062926\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n 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.\",\"PeriodicalId\":15676,\"journal\":{\"name\":\"Journal of Energy Resources Technology-transactions of The Asme\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Energy Resources Technology-transactions of The Asme\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4062926\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Energy Resources Technology-transactions of The Asme","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4062926","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Reliable initial model selection for efficient characterization of channel reservoirs in ensemble Kalman filter
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.
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