View in EDS

Bayesian inversion of seismic and electromagnetic data for marine gas reservoir characterization using multi-chain Markov chain Monte Carlo sampling.

Saved in:
Bibliographic Details
Title: Bayesian inversion of seismic and electromagnetic data for marine gas reservoir characterization using multi-chain Markov chain Monte Carlo sampling.
Authors: Ren, Huiying1 Huiying.Ren@pnnl.gov, Ray, Jaideep2, Hou, Zhangshuan1, Huang, Maoyi1, Bao, Jie1, Swiler, Laura3
Source: Journal of Applied Geophysics. Dec2017, Vol. 147, p68-80. 13p.
Subjects: Bayesian analysis, Inversion (Geophysics), Electromagnetic fields, Imaging systems in seismology, Markov chain Monte Carlo
Abstract: In this study we developed an efficient Bayesian inversion framework for interpreting marine seismic Amplitude Versus Angle and Controlled-Source Electromagnetic data for marine reservoir characterization. The framework uses a multi-chain Markov-chain Monte Carlo sampler, which is a hybrid of DiffeRential Evolution Adaptive Metropolis and Adaptive Metropolis samplers. The inversion framework is tested by estimating reservoir-fluid saturations and porosity based on marine seismic and Controlled-Source Electromagnetic data. The multi-chain Markov-chain Monte Carlo is scalable in terms of the number of chains, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. As a demonstration, the approach is used to efficiently and accurately estimate the porosity and saturations in a representative layered synthetic reservoir. The results indicate that the seismic Amplitude Versus Angle and Controlled-Source Electromagnetic joint inversion provides better estimation of reservoir saturations than the seismic Amplitude Versus Angle only inversion, especially for the parameters in deep layers. The performance of the inversion approach for various levels of noise in observational data was evaluated — reasonable estimates can be obtained with noise levels up to 25%. Sampling efficiency due to the use of multiple chains was also checked and was found to have almost linear scalability. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Applied Geophysics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 126897057
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Bayesian inversion of seismic and electromagnetic data for marine gas reservoir characterization using multi-chain Markov chain Monte Carlo sampling.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Ren%2C+Huiying%22">Ren, Huiying</searchLink><relatesTo>1</relatesTo><i> Huiying.Ren@pnnl.gov</i><br /><searchLink fieldCode="AR" term="%22Ray%2C+Jaideep%22">Ray, Jaideep</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Hou%2C+Zhangshuan%22">Hou, Zhangshuan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Huang%2C+Maoyi%22">Huang, Maoyi</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Bao%2C+Jie%22">Bao, Jie</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Swiler%2C+Laura%22">Swiler, Laura</searchLink><relatesTo>3</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Applied+Geophysics%22">Journal of Applied Geophysics</searchLink>. Dec2017, Vol. 147, p68-80. 13p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Inversion+%28Geophysics%29%22">Inversion (Geophysics)</searchLink><br /><searchLink fieldCode="DE" term="%22Electromagnetic+fields%22">Electromagnetic fields</searchLink><br /><searchLink fieldCode="DE" term="%22Imaging+systems+in+seismology%22">Imaging systems in seismology</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+chain+Monte+Carlo%22">Markov chain Monte Carlo</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this study we developed an efficient Bayesian inversion framework for interpreting marine seismic Amplitude Versus Angle and Controlled-Source Electromagnetic data for marine reservoir characterization. The framework uses a multi-chain Markov-chain Monte Carlo sampler, which is a hybrid of DiffeRential Evolution Adaptive Metropolis and Adaptive Metropolis samplers. The inversion framework is tested by estimating reservoir-fluid saturations and porosity based on marine seismic and Controlled-Source Electromagnetic data. The multi-chain Markov-chain Monte Carlo is scalable in terms of the number of chains, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. As a demonstration, the approach is used to efficiently and accurately estimate the porosity and saturations in a representative layered synthetic reservoir. The results indicate that the seismic Amplitude Versus Angle and Controlled-Source Electromagnetic joint inversion provides better estimation of reservoir saturations than the seismic Amplitude Versus Angle only inversion, especially for the parameters in deep layers. The performance of the inversion approach for various levels of noise in observational data was evaluated — reasonable estimates can be obtained with noise levels up to 25%. Sampling efficiency due to the use of multiple chains was also checked and was found to have almost linear scalability. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Applied Geophysics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=126897057
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.jappgeo.2017.10.004
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 68
    Subjects:
      – SubjectFull: Bayesian analysis
        Type: general
      – SubjectFull: Inversion (Geophysics)
        Type: general
      – SubjectFull: Electromagnetic fields
        Type: general
      – SubjectFull: Imaging systems in seismology
        Type: general
      – SubjectFull: Markov chain Monte Carlo
        Type: general
    Titles:
      – TitleFull: Bayesian inversion of seismic and electromagnetic data for marine gas reservoir characterization using multi-chain Markov chain Monte Carlo sampling.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Ren, Huiying
      – PersonEntity:
          Name:
            NameFull: Ray, Jaideep
      – PersonEntity:
          Name:
            NameFull: Hou, Zhangshuan
      – PersonEntity:
          Name:
            NameFull: Huang, Maoyi
      – PersonEntity:
          Name:
            NameFull: Bao, Jie
      – PersonEntity:
          Name:
            NameFull: Swiler, Laura
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Text: Dec2017
              Type: published
              Y: 2017
          Identifiers:
            – Type: issn-print
              Value: 09269851
          Numbering:
            – Type: volume
              Value: 147
          Titles:
            – TitleFull: Journal of Applied Geophysics
              Type: main
ResultId 1