Constraining a compositional flow model with flow-chemical data using an ensemble-based Kalman filter

Handle URI:
http://hdl.handle.net/10754/552149
Title:
Constraining a compositional flow model with flow-chemical data using an ensemble-based Kalman filter
Authors:
Gharamti, M. E.; Kadoura, A.; Valstar, J.; Sun, S.; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
Isothermal compositional flow models require coupling transient compressible flows and advective transport systems of various chemical species in subsurface porous media. Building such numerical models is quite challenging and may be subject to many sources of uncertainties because of possible incomplete representation of some geological parameters that characterize the system's processes. Advanced data assimilation methods, such as the ensemble Kalman filter (EnKF), can be used to calibrate these models by incorporating available data. In this work, we consider the problem of estimating reservoir permeability using information about phase pressure as well as the chemical properties of fluid components. We carry out state-parameter estimation experiments using joint and dual updating schemes in the context of the EnKF with a two-dimensional single-phase compositional flow model (CFM). Quantitative and statistical analyses are performed to evaluate and compare the performance of the assimilation schemes. Our results indicate that including chemical composition data significantly enhances the accuracy of the permeability estimates. In addition, composition data provide more information to estimate system states and parameters than do standard pressure data. The dual state-parameter estimation scheme provides about 10% more accurate permeability estimates on average than the joint scheme when implemented with the same ensemble members, at the cost of twice more forward model integrations. At similar computational cost, the dual approach becomes only beneficial after using large enough ensembles.
KAUST Department:
Earth Science and Engineering Program; Physical Sciences and Engineering (PSE) Division
Citation:
Constraining a compositional flow model with flow-chemical data using an ensemble-based Kalman filter 2014, 50 (3):2444 Water Resources Research
Publisher:
Wiley-Blackwell
Journal:
Water Resources Research
Issue Date:
Mar-2014
DOI:
10.1002/2013WR014830
Type:
Article
ISSN:
00431397
Additional Links:
http://doi.wiley.com/10.1002/2013WR014830
Appears in Collections:
Articles; Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorGharamti, M. E.en
dc.contributor.authorKadoura, A.en
dc.contributor.authorValstar, J.en
dc.contributor.authorSun, S.en
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-05-04T16:24:08Zen
dc.date.available2015-05-04T16:24:08Zen
dc.date.issued2014-03en
dc.identifier.citationConstraining a compositional flow model with flow-chemical data using an ensemble-based Kalman filter 2014, 50 (3):2444 Water Resources Researchen
dc.identifier.issn00431397en
dc.identifier.doi10.1002/2013WR014830en
dc.identifier.urihttp://hdl.handle.net/10754/552149en
dc.description.abstractIsothermal compositional flow models require coupling transient compressible flows and advective transport systems of various chemical species in subsurface porous media. Building such numerical models is quite challenging and may be subject to many sources of uncertainties because of possible incomplete representation of some geological parameters that characterize the system's processes. Advanced data assimilation methods, such as the ensemble Kalman filter (EnKF), can be used to calibrate these models by incorporating available data. In this work, we consider the problem of estimating reservoir permeability using information about phase pressure as well as the chemical properties of fluid components. We carry out state-parameter estimation experiments using joint and dual updating schemes in the context of the EnKF with a two-dimensional single-phase compositional flow model (CFM). Quantitative and statistical analyses are performed to evaluate and compare the performance of the assimilation schemes. Our results indicate that including chemical composition data significantly enhances the accuracy of the permeability estimates. In addition, composition data provide more information to estimate system states and parameters than do standard pressure data. The dual state-parameter estimation scheme provides about 10% more accurate permeability estimates on average than the joint scheme when implemented with the same ensemble members, at the cost of twice more forward model integrations. At similar computational cost, the dual approach becomes only beneficial after using large enough ensembles.en
dc.publisherWiley-Blackwellen
dc.relation.urlhttp://doi.wiley.com/10.1002/2013WR014830en
dc.rightsArchived with thanks to Water Resources Researchen
dc.titleConstraining a compositional flow model with flow-chemical data using an ensemble-based Kalman filteren
dc.typeArticleen
dc.contributor.departmentEarth Science and Engineering Programen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journalWater Resources Researchen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionSubsurface and Groundwater Systems; Utrecht Netherlandsen
kaust.authorEl Gharamti, Mohamaden
kaust.authorKadoura, Ahmad Salimen
kaust.authorSun, Shuyuen
kaust.authorHoteit, Ibrahimen
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