Show simple item record

dc.contributor.authorEl Gharamti, Mohamad
dc.contributor.authorKadoura, Ahmad Salim
dc.contributor.authorValstar, J.
dc.contributor.authorSun, Shuyu
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2015-05-04T16:24:08Z
dc.date.available2015-05-04T16:24:08Z
dc.date.issued2014-03-19
dc.identifier.citationConstraining a compositional flow model with flow-chemical data using an ensemble-based Kalman filter 2014, 50 (3):2444 Water Resources Research
dc.identifier.issn00431397
dc.identifier.doi10.1002/2013WR014830
dc.identifier.urihttp://hdl.handle.net/10754/552149
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.
dc.publisherAmerican Geophysical Union (AGU)
dc.relation.urlhttp://doi.wiley.com/10.1002/2013WR014830
dc.rightsArchived with thanks to Water Resources Research
dc.titleConstraining a compositional flow model with flow-chemical data using an ensemble-based Kalman filter
dc.typeArticle
dc.contributor.departmentComputational Transport Phenomena Lab
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalWater Resources Research
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionSubsurface and Groundwater Systems; Utrecht Netherlands
kaust.personEl Gharamti, Mohamad
kaust.personKadoura, Ahmad Salim
kaust.personSun, Shuyu
kaust.personHoteit, Ibrahim
refterms.dateFOA2018-06-13T18:11:16Z
dc.date.published-online2014-03-19
dc.date.published-print2014-03


Files in this item

Thumbnail
Name:
wrcr20827.pdf
Size:
23.37Mb
Format:
PDF
Description:
Main article

This item appears in the following Collection(s)

Show simple item record