ASSIMILATION OF COARSE-SCALEDATAUSINGTHE ENSEMBLE KALMAN FILTER

Handle URI:
http://hdl.handle.net/10754/597615
Title:
ASSIMILATION OF COARSE-SCALEDATAUSINGTHE ENSEMBLE KALMAN FILTER
Authors:
Efendiev, Yalchin; Datta-Gupta, A.; Akella, Santha
Abstract:
Reservoir data is usually scale dependent and exhibits multiscale features. In this paper we use the ensemble Kalman filter (EnKF) to integrate data at different spatial scales for estimating reservoir fine-scale characteristics. Relationships between the various scales is modeled via upscaling techniques. We propose two versions of the EnKF to assimilate the multiscale data, (i) where all the data are assimilated together and (ii) the data are assimilated sequentially in batches. Ensemble members obtained after assimilating one set of data are used as a prior to assimilate the next set of data. Both of these versions are easily implementable with any other upscaling which links the fine to the coarse scales. The numerical results with different methods are presented in a twin experiment setup using a two-dimensional, two-phase (oil and water) flow model. Results are shown with coarse-scale permeability and coarse-scale saturation data. They indicate that additional data provides better fine-scale estimates and fractional flow predictions. We observed that the two versions of the EnKF differed in their estimates when coarse-scale permeability is provided, whereas their results are similar when coarse-scale saturation is used. This behavior is thought to be due to the nonlinearity of the upscaling operator in the case of the former data. We also tested our procedures with various precisions of the coarse-scale data to account for the inexact relationship between the fine and coarse scale data. As expected, the results show that higher precision in the coarse-scale data yielded improved estimates. With better coarse-scale modeling and inversion techniques as more data at multiple coarse scales is made available, the proposed modification to the EnKF could be relevant in future studies.
Citation:
Efendiev Y, Datta-Gupta A, Akella S (2011) ASSIMILATION OF COARSE-SCALEDATAUSINGTHE ENSEMBLE KALMAN FILTER. International Journal for Uncertainty Quantification 1: 49–76. Available: http://dx.doi.org/10.1615/Int.J.UncertaintyQuantification.v1.i1.40.
Publisher:
Begell House
Journal:
International Journal for Uncertainty Quantification
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
2011
DOI:
10.1615/Int.J.UncertaintyQuantification.v1.i1.40
Type:
Article
ISSN:
2152-5080
Sponsors:
The work of Akhil Datta-Gupta and Yalchin Efendiev is partially supported by DOE (DE-FG03-00ER15034), NSF CMG 0724704, and KAUST award number KUS-C1-016-04.
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Full metadata record

DC FieldValue Language
dc.contributor.authorEfendiev, Yalchinen
dc.contributor.authorDatta-Gupta, A.en
dc.contributor.authorAkella, Santhaen
dc.date.accessioned2016-02-25T12:43:06Zen
dc.date.available2016-02-25T12:43:06Zen
dc.date.issued2011en
dc.identifier.citationEfendiev Y, Datta-Gupta A, Akella S (2011) ASSIMILATION OF COARSE-SCALEDATAUSINGTHE ENSEMBLE KALMAN FILTER. International Journal for Uncertainty Quantification 1: 49–76. Available: http://dx.doi.org/10.1615/Int.J.UncertaintyQuantification.v1.i1.40.en
dc.identifier.issn2152-5080en
dc.identifier.doi10.1615/Int.J.UncertaintyQuantification.v1.i1.40en
dc.identifier.urihttp://hdl.handle.net/10754/597615en
dc.description.abstractReservoir data is usually scale dependent and exhibits multiscale features. In this paper we use the ensemble Kalman filter (EnKF) to integrate data at different spatial scales for estimating reservoir fine-scale characteristics. Relationships between the various scales is modeled via upscaling techniques. We propose two versions of the EnKF to assimilate the multiscale data, (i) where all the data are assimilated together and (ii) the data are assimilated sequentially in batches. Ensemble members obtained after assimilating one set of data are used as a prior to assimilate the next set of data. Both of these versions are easily implementable with any other upscaling which links the fine to the coarse scales. The numerical results with different methods are presented in a twin experiment setup using a two-dimensional, two-phase (oil and water) flow model. Results are shown with coarse-scale permeability and coarse-scale saturation data. They indicate that additional data provides better fine-scale estimates and fractional flow predictions. We observed that the two versions of the EnKF differed in their estimates when coarse-scale permeability is provided, whereas their results are similar when coarse-scale saturation is used. This behavior is thought to be due to the nonlinearity of the upscaling operator in the case of the former data. We also tested our procedures with various precisions of the coarse-scale data to account for the inexact relationship between the fine and coarse scale data. As expected, the results show that higher precision in the coarse-scale data yielded improved estimates. With better coarse-scale modeling and inversion techniques as more data at multiple coarse scales is made available, the proposed modification to the EnKF could be relevant in future studies.en
dc.description.sponsorshipThe work of Akhil Datta-Gupta and Yalchin Efendiev is partially supported by DOE (DE-FG03-00ER15034), NSF CMG 0724704, and KAUST award number KUS-C1-016-04.en
dc.publisherBegell Houseen
dc.titleASSIMILATION OF COARSE-SCALEDATAUSINGTHE ENSEMBLE KALMAN FILTERen
dc.typeArticleen
dc.identifier.journalInternational Journal for Uncertainty Quantificationen
dc.contributor.institutionThe Johns Hopkins University, Baltimore, MD 21218, USAen
dc.contributor.institutionDepartment of Petroleum Engineering, Texas A&M University, College Station, TX 77843, USAen
dc.contributor.institutionCenter for Numerical Porous Media (NumPor), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabiaen
dc.contributor.institutionDepartment of Mathematics & Institute for Scientific Computation (ISC), Texas A&M Universityen
kaust.grant.numberKUS-C1-016-04en
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