{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1360855571127745280.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.2118/108438-pa"}},{"identifier":{"@type":"URI","@value":"https://onepetro.org/SJ/article-pdf/12/04/438/2561862/spe-108438-pa.pdf"}}],"dc:title":[{"@value":"An Iterative Ensemble Kalman Filter for Multiphase Fluid Flow Data Assimilation"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:title>Summary</jats:title>\n               <jats:p>The dynamical equations for multiphase flow in porous media are highly non-linear and the number of variables required to characterize the medium is usually large, often two or more variables per simulator gridblock. Neither the extended Kalman filter nor the ensemble Kalman filter is suitable for assimilating data or for characterizing uncertainty for this type of problem. Although the ensemble Kalman filter handles the nonlinear dynamics correctly during the forecast step, it sometimes fails badly in the analysis (or updating) of saturations.</jats:p>\n               <jats:p>This paper focuses on the use of an iterative ensemble Kalman filter for data assimilation in nonlinear problems, especially of the type related to multiphase flow in porous media. Two issues are key: (1) iteration to enforce constraints and (2) ensuring that the resulting ensemble is representative of the conditional pdf (i.e. that the uncertainty quantification is correct). The new algorithm is compared to the ensemble Kalman filter on several highly nonlinear example problems, and shown to be superior in the prediction of uncertainty.</jats:p>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1380855571127745280","@type":"Researcher","foaf:name":[{"@value":"Yaqing Gu"}],"jpcoar:affiliationName":[{"@value":"BP"}]},{"@id":"https://cir.nii.ac.jp/crid/1380855571127745281","@type":"Researcher","foaf:name":[{"@value":"Dean S. Oliver"}],"jpcoar:affiliationName":[{"@value":"U. of Oklahoma"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"1086055X"},{"@type":"EISSN","@value":"19300220"}],"prism:publicationName":[{"@value":"SPE Journal"}],"dc:publisher":[{"@value":"Society of Petroleum Engineers (SPE)"}],"prism:publicationDate":"2007-12-20","prism:volume":"12","prism:number":"04","prism:startingPage":"438","prism:endingPage":"446"},"reviewed":"false","url":[{"@id":"https://onepetro.org/SJ/article-pdf/12/04/438/2561862/spe-108438-pa.pdf"}],"createdAt":"2009-02-24","modifiedAt":"2021-12-22","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1050294045368942976","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@language":"en","@value":"A candidate secular variation model for IGRF-13 based on MHD dynamo simulation and 4DEnVar data assimilation"}]},{"@id":"https://cir.nii.ac.jp/crid/1360576118692383360","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Behavior of the iterative ensemble-based variational method in nonlinear problems"}]},{"@id":"https://cir.nii.ac.jp/crid/1360864738727947904","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Application of Exact Newton Optimisation to the Maximum Likelihood Ensemble Filter"}]},{"@id":"https://cir.nii.ac.jp/crid/1390282680165649920","@type":"Article","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Relative Permeability Estimation by Ensemble Kalman Filter Using Function Transformation"},{"@language":"ja","@value":"関数変換を用いたアンサンブルカルマンフィルターによる相対浸透率の推定"},{"@language":"ja-Kana","@value":"カンスウ ヘンカン オ モチイタ アンサンブルカルマンフィルター ニ ヨル ソウタイ シントウリツ ノ スイテイ"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.2118/108438-pa"},{"@type":"CROSSREF","@value":"10.1627/jpi.52.248_references_DOI_QVoLjRF1sYegasnqIkXrq9R8BOy"},{"@type":"CROSSREF","@value":"10.1186/s40623-020-01253-8_references_DOI_QVoLjRF1sYegasnqIkXrq9R8BOy"},{"@type":"CROSSREF","@value":"10.5194/npg-28-93-2021_references_DOI_QVoLjRF1sYegasnqIkXrq9R8BOy"},{"@type":"CROSSREF","@value":"10.16993/tellusa.3255_references_DOI_QVoLjRF1sYegasnqIkXrq9R8BOy"}]}