Enhanced characterization of reservoir hydrocarbon components using electromagnetic data attributes

Handle URI:
http://hdl.handle.net/10754/592603
Title:
Enhanced characterization of reservoir hydrocarbon components using electromagnetic data attributes
Authors:
Katterbauer, Klemens ( 0000-0003-0931-8843 ) ; Arango, Santiago; Sun, Shuyu ( 0000-0002-3078-864X ) ; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
Advances in electromagnetic imaging techniques have led to the growing utilization of this technology for reservoir monitoring and exploration. These exploit the strong conductivity contrast between the hydrocarbon and water phases and have been used for mapping water front propagation in hydrocarbon reservoirs and enhancing the characterization of the reservoir formation. The conventional approach for the integration of electromagnetic data is to invert the data for saturation properties and then subsequently use the inverted properties as constraints in the history matching process. The non-uniqueness and measurement errors may however make this electromagnetic inversion problem strongly ill-posed, leading to potentially inaccurate saturation profiles. Another limitation of this approach is the uncertainty of Archie's parameters in relating rock conductivity to water saturation, which may vary in the reservoir and are generally poorly known. We present an Ensemble Kalman Filter framework for efficiently integrating electromagnetic data into the history matching process and for simultaneously estimating the Archie's parameters and the variance of the observation error of the electromagnetic data. We apply the proposed framework to a compositional reservoir model. We aim at assessing the relevance of EM data for estimating the different hydrocarbon components of the reservoir. The experimental results demonstrate that the individual hydrocarbon components are generally well matched, with nitrogen exhibiting the strongest improvement. The estimated observation error standard deviations are also within expected levels (between 5 and 10%), significantly contributing to the robustness of the proposed EM history matching framework. Archie's parameter estimates approximate well the reference profile and assist in the accurate description of the electrical conductivity properties of the reservoir formation, hence leading to estimation accuracy improvements of around 15%.
KAUST Department:
Earth Science and Engineering Program
Citation:
Enhanced characterization of reservoir hydrocarbon components using electromagnetic data attributes 2015 Journal of Petroleum Science and Engineering
Publisher:
Elsevier BV
Journal:
Journal of Petroleum Science and Engineering
Issue Date:
23-Dec-2015
DOI:
10.1016/j.petrol.2015.12.015
Type:
Article
ISSN:
09204105
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S0920410515302242
Appears in Collections:
Articles; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorKatterbauer, Klemensen
dc.contributor.authorArango, Santiagoen
dc.contributor.authorSun, Shuyuen
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-12-27T13:24:17Zen
dc.date.available2015-12-27T13:24:17Zen
dc.date.issued2015-12-23en
dc.identifier.citationEnhanced characterization of reservoir hydrocarbon components using electromagnetic data attributes 2015 Journal of Petroleum Science and Engineeringen
dc.identifier.issn09204105en
dc.identifier.doi10.1016/j.petrol.2015.12.015en
dc.identifier.urihttp://hdl.handle.net/10754/592603en
dc.description.abstractAdvances in electromagnetic imaging techniques have led to the growing utilization of this technology for reservoir monitoring and exploration. These exploit the strong conductivity contrast between the hydrocarbon and water phases and have been used for mapping water front propagation in hydrocarbon reservoirs and enhancing the characterization of the reservoir formation. The conventional approach for the integration of electromagnetic data is to invert the data for saturation properties and then subsequently use the inverted properties as constraints in the history matching process. The non-uniqueness and measurement errors may however make this electromagnetic inversion problem strongly ill-posed, leading to potentially inaccurate saturation profiles. Another limitation of this approach is the uncertainty of Archie's parameters in relating rock conductivity to water saturation, which may vary in the reservoir and are generally poorly known. We present an Ensemble Kalman Filter framework for efficiently integrating electromagnetic data into the history matching process and for simultaneously estimating the Archie's parameters and the variance of the observation error of the electromagnetic data. We apply the proposed framework to a compositional reservoir model. We aim at assessing the relevance of EM data for estimating the different hydrocarbon components of the reservoir. The experimental results demonstrate that the individual hydrocarbon components are generally well matched, with nitrogen exhibiting the strongest improvement. The estimated observation error standard deviations are also within expected levels (between 5 and 10%), significantly contributing to the robustness of the proposed EM history matching framework. Archie's parameter estimates approximate well the reference profile and assist in the accurate description of the electrical conductivity properties of the reservoir formation, hence leading to estimation accuracy improvements of around 15%.en
dc.language.isoenen
dc.publisherElsevier BVen
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0920410515302242en
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Petroleum Science and Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Petroleum Science and Engineering, 23 December 2015. DOI: 10.1016/j.petrol.2015.12.015en
dc.subjectHistory matchingen
dc.subjectElectromagnetic Dataen
dc.subjectObservation error covariance estimationen
dc.subjectArchie parameter estimationen
dc.subjectEnsemble based history matchingen
dc.titleEnhanced characterization of reservoir hydrocarbon components using electromagnetic data attributesen
dc.typeArticleen
dc.contributor.departmentEarth Science and Engineering Programen
dc.identifier.journalJournal of Petroleum Science and Engineeringen
dc.eprint.versionPost-printen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorKatterbauer, Klemensen
kaust.authorArango, Santiagoen
kaust.authorSun, Shuyuen
kaust.authorHoteit, Ibrahimen
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