Orthogonal Matching Pursuit for Enhanced Recovery of Sparse Geological Structures With the Ensemble Kalman Filter

Handle URI:
http://hdl.handle.net/10754/614420
Title:
Orthogonal Matching Pursuit for Enhanced Recovery of Sparse Geological Structures With 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:
Estimating the locations and the structures of subsurface channels holds significant importance for forecasting the subsurface flow and reservoir productivity. These channels exhibit high permeability and are easily contrasted from the low-permeability rock formations in their surroundings. This enables formulating the flow channels estimation problem as a sparse field recovery problem. The ensemble Kalman filter (EnKF) is a widely used technique for the estimation and calibration of subsurface reservoir model parameters, such as permeability. However, the conventional EnKF framework does not provide an efficient mechanism to incorporate prior information on the wide varieties of subsurface geological structures, and often fails to recover and preserve flow channel structures. Recent works in the area of compressed sensing (CS) have shown that estimating in a sparse domain, using algorithms such as the orthogonal matching pursuit (OMP), may significantly improve the estimation quality when dealing with such problems. We propose two new, and computationally efficient, algorithms combining OMP with the EnKF to improve the estimation and recovery of the subsurface geological channels. Numerical experiments suggest that the proposed algorithms provide efficient mechanisms to incorporate and preserve structural information in the EnKF and result in significant improvements in recovering flow channel structures.
KAUST Department:
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division; Physical Sciences and Engineering (PSE) Division
Citation:
Orthogonal Matching Pursuit for Enhanced Recovery of Sparse Geological Structures With the Ensemble Kalman Filter 2016, 9 (4):1710 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue Date:
23-Feb-2016
DOI:
10.1109/JSTARS.2016.2518119
Type:
Article
ISSN:
1939-1404; 2151-1535
Sponsors:
This work was funded in part by a CRG2 grant CRG\_ R2\_13\_ALOU\_KAUST\_2 from the Office of Competitive Research (OCRF) at King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7416151
Appears in Collections:
Articles

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.accessioned2016-06-23T11:08:21Z-
dc.date.available2016-06-23T11:08:21Z-
dc.date.issued2016-02-23-
dc.identifier.citationOrthogonal Matching Pursuit for Enhanced Recovery of Sparse Geological Structures With the Ensemble Kalman Filter 2016, 9 (4):1710 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensingen
dc.identifier.issn1939-1404-
dc.identifier.issn2151-1535-
dc.identifier.doi10.1109/JSTARS.2016.2518119-
dc.identifier.urihttp://hdl.handle.net/10754/614420-
dc.description.abstractEstimating the locations and the structures of subsurface channels holds significant importance for forecasting the subsurface flow and reservoir productivity. These channels exhibit high permeability and are easily contrasted from the low-permeability rock formations in their surroundings. This enables formulating the flow channels estimation problem as a sparse field recovery problem. The ensemble Kalman filter (EnKF) is a widely used technique for the estimation and calibration of subsurface reservoir model parameters, such as permeability. However, the conventional EnKF framework does not provide an efficient mechanism to incorporate prior information on the wide varieties of subsurface geological structures, and often fails to recover and preserve flow channel structures. Recent works in the area of compressed sensing (CS) have shown that estimating in a sparse domain, using algorithms such as the orthogonal matching pursuit (OMP), may significantly improve the estimation quality when dealing with such problems. We propose two new, and computationally efficient, algorithms combining OMP with the EnKF to improve the estimation and recovery of the subsurface geological channels. Numerical experiments suggest that the proposed algorithms provide efficient mechanisms to incorporate and preserve structural information in the EnKF and result in significant improvements in recovering flow channel structures.en
dc.description.sponsorshipThis work was funded in part by a CRG2 grant CRG\_ R2\_13\_ALOU\_KAUST\_2 from the Office of Competitive Research (OCRF) at King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7416151en
dc.rights(c) 2016 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 sensing (CS)en
dc.subjectK-SVDen
dc.subjectensemble Kalman filter (EnKF)en
dc.subjectorthogonal matching pursuit (OMP)en
dc.subjectsparsityen
dc.subjectstate-parameter estimationen
dc.subjectsubsurface characterizationen
dc.titleOrthogonal Matching Pursuit for Enhanced Recovery of Sparse Geological Structures With the Ensemble Kalman Filteren
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Divisionen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensingen
dc.eprint.versionPost-printen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorSana, Furrukhen
kaust.authorKatterbauer, Klemensen
kaust.authorAl-Naffouri, Tareq Y.en
kaust.authorHoteit, Ibrahimen
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