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dc.contributor.authorSana, Furrukh
dc.contributor.authorKatterbauer, Klemens
dc.contributor.authorAl-Naffouri, Tareq Y.
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2015-12-07T10:17:54Z
dc.date.available2015-12-07T10:17:54Z
dc.date.issued2015-11-12
dc.identifier.doi10.1109/IGARSS.2015.7326474
dc.identifier.urihttp://hdl.handle.net/10754/583292
dc.description.abstractRecovering information on subsurface geological features, such as flow channels, holds significant importance for optimizing the productivity of oil reservoirs. The flow channels exhibit high permeability in contrast to low permeability rock formations in their surroundings, enabling formulation of a sparse field recovery problem. The Ensemble Kalman filter (EnKF) is a widely used technique for the estimation of subsurface parameters, such as permeability. However, the EnKF often fails to recover and preserve the channel structures during the estimation process. Compressed Sensing (CS) has shown to significantly improve the reconstruction quality when dealing with such problems. We propose a new scheme based on CS principles to enhance the reconstruction of subsurface geological features by transforming the EnKF estimation process to a sparse domain representing diverse geological structures. Numerical experiments suggest that the proposed scheme provides an efficient mechanism to incorporate and preserve structural information in the estimation process and results in significant enhancement in the recovery of flow channel structures.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7326474
dc.rights(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectCompressed Sensing
dc.subjectEnsemble Kalman Filter
dc.subjectK-SVD
dc.subjectOrthogonal Matching Pursuit
dc.subjectSubsurface Characterization
dc.titleEnhanced recovery of subsurface geological structures using compressed sensing and the Ensemble Kalman filter
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journal2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
dc.conference.date26-31 July 2015
dc.conference.name2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
dc.conference.locationMilan, Italy
dc.eprint.versionPost-print
kaust.personSana, Furrukh
kaust.personKatterbauer, Klemens
kaust.personAl-Naffouri, Tareq Y.
kaust.personHoteit, Ibrahim
refterms.dateFOA2018-06-13T11:55:32Z
dc.date.published-online2015-11-12
dc.date.published-print2015-07


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