Data assimilation using Bayesian filters and B-spline geological models

Handle URI:
http://hdl.handle.net/10754/554389
Title:
Data assimilation using Bayesian filters and B-spline geological models
Authors:
Duan, Lian; Farmer, Chris; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Luo, Xiaodong; Moroz, Irene
Abstract:
This paper proposes a new approach to problems of data assimilation, also known as history matching, of oilfield production data by adjustment of the location and sharpness of patterns of geological facies. Traditionally, this problem has been addressed using gradient based approaches with a level set parameterization of the geology. Gradient-based methods are robust, but computationally demanding with real-world reservoir problems and insufficient for reservoir management uncertainty assessment. Recently, the ensemble filter approach has been used to tackle this problem because of its high efficiency from the standpoint of implementation, computational cost, and performance. Incorporation of level set parameterization in this approach could further deal with the lack of differentiability with respect to facies type, but its practical implementation is based on some assumptions that are not easily satisfied in real problems. In this work, we propose to describe the geometry of the permeability field using B-spline curves. This transforms history matching of the discrete facies type to the estimation of continuous B-spline control points. As filtering scheme, we use the ensemble square-root filter (EnSRF). The efficacy of the EnSRF with the B-spline parameterization is investigated through three numerical experiments, in which the reservoir contains a curved channel, a disconnected channel or a 2-dimensional closed feature. It is found that the application of the proposed method to the problem of adjusting facies edges to match production data is relatively straightforward and provides statistical estimates of the distribution of geological facies and of the state of the reservoir.
KAUST Department:
Physical Sciences and Engineering (PSE) Division
Citation:
Data assimilation using Bayesian filters and B-spline geological models 2011, 290:012004 Journal of Physics: Conference Series
Publisher:
IOP Publishing
Journal:
Journal of Physics: Conference Series
Conference/Event name:
5th International Conference on Inverse Problems 2010
Issue Date:
1-Apr-2011
DOI:
10.1088/1742-6596/290/1/012004
Type:
Conference Paper
ISSN:
1742-6596
Additional Links:
http://stacks.iop.org/1742-6596/290/i=1/a=012004?key=crossref.71e007cb63920e4d70746a5730022d3a
Appears in Collections:
Conference Papers; Physical Sciences and Engineering (PSE) Division; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorDuan, Lianen
dc.contributor.authorFarmer, Chrisen
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorLuo, Xiaodongen
dc.contributor.authorMoroz, Ireneen
dc.date.accessioned2015-05-21T07:09:05Zen
dc.date.available2015-05-21T07:09:05Zen
dc.date.issued2011-04-01en
dc.identifier.citationData assimilation using Bayesian filters and B-spline geological models 2011, 290:012004 Journal of Physics: Conference Seriesen
dc.identifier.issn1742-6596en
dc.identifier.doi10.1088/1742-6596/290/1/012004en
dc.identifier.urihttp://hdl.handle.net/10754/554389en
dc.description.abstractThis paper proposes a new approach to problems of data assimilation, also known as history matching, of oilfield production data by adjustment of the location and sharpness of patterns of geological facies. Traditionally, this problem has been addressed using gradient based approaches with a level set parameterization of the geology. Gradient-based methods are robust, but computationally demanding with real-world reservoir problems and insufficient for reservoir management uncertainty assessment. Recently, the ensemble filter approach has been used to tackle this problem because of its high efficiency from the standpoint of implementation, computational cost, and performance. Incorporation of level set parameterization in this approach could further deal with the lack of differentiability with respect to facies type, but its practical implementation is based on some assumptions that are not easily satisfied in real problems. In this work, we propose to describe the geometry of the permeability field using B-spline curves. This transforms history matching of the discrete facies type to the estimation of continuous B-spline control points. As filtering scheme, we use the ensemble square-root filter (EnSRF). The efficacy of the EnSRF with the B-spline parameterization is investigated through three numerical experiments, in which the reservoir contains a curved channel, a disconnected channel or a 2-dimensional closed feature. It is found that the application of the proposed method to the problem of adjusting facies edges to match production data is relatively straightforward and provides statistical estimates of the distribution of geological facies and of the state of the reservoir.en
dc.publisherIOP Publishingen
dc.relation.urlhttp://stacks.iop.org/1742-6596/290/i=1/a=012004?key=crossref.71e007cb63920e4d70746a5730022d3aen
dc.rightsArchived with thanks to Journal of Physics: Conference Series http://creativecommons.org/licenses/by/3.0/en
dc.titleData assimilation using Bayesian filters and B-spline geological modelsen
dc.typeConference Paperen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journalJournal of Physics: Conference Seriesen
dc.conference.date2010-12-13 to 2010-12-17en
dc.conference.name5th International Conference on Inverse Problems 2010en
dc.conference.locationHong Kong, HKGen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionOxford Centre for Collaborative Applied Mathematics, Mathematical Institute, University of Oxford, 24 – 29 St Giles, Oxford, OX1 3LBen
dc.contributor.institutionOxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, 24 – 29 St Giles, Oxford, OX1 3LBen
kaust.authorHoteit, Ibrahimen
kaust.authorLuo, Xiaodongen
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