A Sparse Bayesian Imaging Technique for Efficient Recovery of Reservoir Channels With Time-Lapse Seismic Measurements

Handle URI:
http://hdl.handle.net/10754/614425
Title:
A Sparse Bayesian Imaging Technique for Efficient Recovery of Reservoir Channels With Time-Lapse Seismic Measurements
Authors:
Sana, Furrukh ( 0000-0002-6712-9357 ) ; Ravanelli, Fabio; Al-Naffouri, Tareq Y.; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
Subsurface reservoir flow channels are characterized by high-permeability values and serve as preferred pathways for fluid propagation. Accurate estimation of their geophysical structures is thus of great importance for the oil industry. The ensemble Kalman filter (EnKF) is a widely used statistical technique for estimating subsurface reservoir model parameters. However, accurate reconstruction of the subsurface geological features with the EnKF is challenging because of the limited measurements available from the wells and the smoothing effects imposed by the \ell _{2} -norm nature of its update step. A new EnKF scheme based on sparse domain representation was introduced by Sana et al. (2015) to incorporate useful prior structural information in the estimation process for efficient recovery of subsurface channels. In this paper, we extend this work in two ways: 1) investigate the effects of incorporating time-lapse seismic data on the channel reconstruction; and 2) explore a Bayesian sparse reconstruction algorithm with the potential ability to reduce the computational requirements. Numerical results suggest that the performance of the new sparse Bayesian based EnKF scheme is enhanced with the availability of seismic measurements, leading to further improvement in the recovery of flow channels structures. The sparse Bayesian approach further provides a computationally efficient framework for enforcing a sparse solution, especially with the possibility of using high sparsity rates through the inclusion of seismic data.
KAUST Department:
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division; Physical Sciences and Engineering (PSE) Division
Citation:
A Sparse Bayesian Imaging Technique for Efficient Recovery of Reservoir Channels With Time-Lapse Seismic Measurements 2016:1 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:
1-Jun-2016
DOI:
10.1109/JSTARS.2016.2563163
Type:
Article
ISSN:
1939-1404; 2151-1535
Sponsors:
This work was supported in part by a CRG2 grant CRG_R2_13_ALOU_KAUST_2 from the Office of Competitive Research at the King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7482797
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorSana, Furrukhen
dc.contributor.authorRavanelli, Fabioen
dc.contributor.authorAl-Naffouri, Tareq Y.en
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2016-06-23T11:16:59Z-
dc.date.available2016-06-23T11:16:59Z-
dc.date.issued2016-06-01-
dc.identifier.citationA Sparse Bayesian Imaging Technique for Efficient Recovery of Reservoir Channels With Time-Lapse Seismic Measurements 2016:1 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.2016.2563163-
dc.identifier.urihttp://hdl.handle.net/10754/614425-
dc.description.abstractSubsurface reservoir flow channels are characterized by high-permeability values and serve as preferred pathways for fluid propagation. Accurate estimation of their geophysical structures is thus of great importance for the oil industry. The ensemble Kalman filter (EnKF) is a widely used statistical technique for estimating subsurface reservoir model parameters. However, accurate reconstruction of the subsurface geological features with the EnKF is challenging because of the limited measurements available from the wells and the smoothing effects imposed by the \ell _{2} -norm nature of its update step. A new EnKF scheme based on sparse domain representation was introduced by Sana et al. (2015) to incorporate useful prior structural information in the estimation process for efficient recovery of subsurface channels. In this paper, we extend this work in two ways: 1) investigate the effects of incorporating time-lapse seismic data on the channel reconstruction; and 2) explore a Bayesian sparse reconstruction algorithm with the potential ability to reduce the computational requirements. Numerical results suggest that the performance of the new sparse Bayesian based EnKF scheme is enhanced with the availability of seismic measurements, leading to further improvement in the recovery of flow channels structures. The sparse Bayesian approach further provides a computationally efficient framework for enforcing a sparse solution, especially with the possibility of using high sparsity rates through the inclusion of seismic data.en
dc.description.sponsorshipThis work was supported in part by a CRG2 grant CRG_R2_13_ALOU_KAUST_2 from the Office of Competitive Research at the King Abdullah University of Science and Technology, 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=7482797en
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.subjectEnsemble Kalman filter (EnKF)en
dc.subjectK-SVDen
dc.subjectorthogonal matching pursuit (OMP)en
dc.subjectreservoir history matchingen
dc.subjectseismic imagingen
dc.subjectsparsityen
dc.subjectsubsurface channels recoveryen
dc.subjectsupport agnostic Bayesian matching pursuit (SABMP)en
dc.titleA Sparse Bayesian Imaging Technique for Efficient Recovery of Reservoir Channels With Time-Lapse Seismic Measurementsen
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.institutionSaudi Aramco, Dhahran 31311, Saudi Arabiaen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorSana, Furrukhen
kaust.authorAl-Naffouri, Tareq Y.en
kaust.authorHoteit, Ibrahimen
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