Enhanced recovery of subsurface geological structures using compressed sensing and the Ensemble Kalman filter

Handle URI:
http://hdl.handle.net/10754/583292
Title:
Enhanced recovery of subsurface geological structures using compressed sensing and the Ensemble Kalman filter
Authors:
Sana, Furrukh ( 0000-0002-6712-9357 ) ; Katterbauer, Klemens ( 0000-0003-0931-8843 ) ; Al-Naffouri, Tareq Y.; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
Recovering 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Physical Sciences and Engineering (PSE) Division
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Conference/Event name:
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Issue Date:
26-Jul-2015
DOI:
10.1109/IGARSS.2015.7326474
Type:
Conference Paper
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7326474
Appears in Collections:
Conference Papers; Physical Sciences and Engineering (PSE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSana, Furrukhen
dc.contributor.authorKatterbauer, Klemensen
dc.contributor.authorAl-Naffouri, Tareq Y.en
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-12-07T10:17:54Zen
dc.date.available2015-12-07T10:17:54Zen
dc.date.issued2015-07-26en
dc.identifier.doi10.1109/IGARSS.2015.7326474en
dc.identifier.urihttp://hdl.handle.net/10754/583292en
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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7326474en
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.en
dc.subjectCompressed Sensingen
dc.subjectEnsemble Kalman Filteren
dc.subjectK-SVDen
dc.subjectOrthogonal Matching Pursuiten
dc.subjectSubsurface Characterizationen
dc.titleEnhanced recovery of subsurface geological structures using compressed sensing and the Ensemble Kalman filteren
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journal2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)en
dc.conference.date26-31 July 2015en
dc.conference.name2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)en
dc.conference.locationMilan, Italyen
dc.eprint.versionPost-printen
kaust.authorSana, Furrukhen
kaust.authorKatterbauer, Klemensen
kaust.authorAl-Naffouri, Tareq Y.en
kaust.authorHoteit, Ibrahimen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.