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A Multi-Point Geostatistical Seismic Inversion Method Based on Local Probability Updating of Lithofacies.

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Title: A Multi-Point Geostatistical Seismic Inversion Method Based on Local Probability Updating of Lithofacies.
Authors: Wang, Zhihong1 (AUTHOR) wzh2331@sina.com, Chen, Tiansheng2 (AUTHOR) chents.syky@sinopec.com, Hu, Xun3 (AUTHOR) 2020310040@student.cup.edu.cn, Wang, Lixin4,5 (AUTHOR) 201571323@yangtzeu.edu.cn, Yin, Yanshu4,5 (AUTHOR) wzh2331@sina.com
Source: Energies (19961073). Jan2022, Vol. 15 Issue 1, p299. 1p.
Subject Terms: *Lithofacies, *Random noise theory, *Probability theory, *Statistical sampling, *Facies, *Test methods, *Problem solving
Geographic Terms: China
Abstract: In order to solve the problem that elastic parameter constraints are not taken into account in local lithofacies updating in multi-point geostatistical inversion, a new multi-point geostatistical inversion method with local facies updating under seismic elastic constraints is proposed. The main improvement of the method is that the probability of multi-point facies modeling is combined with the facies probability reflected by the optimal elastic parameters retained from the previous inversion to predict and update the current lithofacies model. Constrained by the current lithofacies model, the elastic parameters were obtained via direct sampling based on the statistical relationship between the lithofacies and the elastic parameters. Forward simulation records were generated via convolution and were compared with the actual seismic records to obtain the optimal lithofacies and elastic parameters. The inversion method adopts the internal and external double cycle iteration mechanism, and the internal cycle updates and inverts the local lithofacies. The outer cycle determines whether the correlation between the entire seismic record and the actual seismic record meets the given conditions, and the cycle iterates until the given conditions are met in order to achieve seismic inversion prediction. The theoretical model of the Stanford Center for Reservoir Forecasting and the practical model of the Xinchang gas field in western China were used to test the new method. The results show that the correlation between the synthetic seismic records and the actual seismic records is the best, and the lithofacies matching degree of the inversion is the highest. The results of the conventional multi-point geostatistical inversion are the next best, and the results of the two-point geostatistical inversion are the worst. The results show that the reservoir parameters obtained using the local probability updating of lithofacies method are closer to the actual reservoir parameters. This method is worth popularizing in practical exploration and development. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Multi-Point Geostatistical Seismic Inversion Method Based on Local Probability Updating of Lithofacies.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Zhihong%22">Wang, Zhihong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wzh2331@sina.com</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Tiansheng%22">Chen, Tiansheng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> chents.syky@sinopec.com</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Xun%22">Hu, Xun</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> 2020310040@student.cup.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Lixin%22">Wang, Lixin</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<i> 201571323@yangtzeu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yin%2C+Yanshu%22">Yin, Yanshu</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<i> wzh2331@sina.com</i>
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  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jan2022, Vol. 15 Issue 1, p299. 1p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Lithofacies%22">Lithofacies</searchLink><br />*<searchLink fieldCode="DE" term="%22Random+noise+theory%22">Random noise theory</searchLink><br />*<searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistical+sampling%22">Statistical sampling</searchLink><br />*<searchLink fieldCode="DE" term="%22Facies%22">Facies</searchLink><br />*<searchLink fieldCode="DE" term="%22Test+methods%22">Test methods</searchLink><br />*<searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In order to solve the problem that elastic parameter constraints are not taken into account in local lithofacies updating in multi-point geostatistical inversion, a new multi-point geostatistical inversion method with local facies updating under seismic elastic constraints is proposed. The main improvement of the method is that the probability of multi-point facies modeling is combined with the facies probability reflected by the optimal elastic parameters retained from the previous inversion to predict and update the current lithofacies model. Constrained by the current lithofacies model, the elastic parameters were obtained via direct sampling based on the statistical relationship between the lithofacies and the elastic parameters. Forward simulation records were generated via convolution and were compared with the actual seismic records to obtain the optimal lithofacies and elastic parameters. The inversion method adopts the internal and external double cycle iteration mechanism, and the internal cycle updates and inverts the local lithofacies. The outer cycle determines whether the correlation between the entire seismic record and the actual seismic record meets the given conditions, and the cycle iterates until the given conditions are met in order to achieve seismic inversion prediction. The theoretical model of the Stanford Center for Reservoir Forecasting and the practical model of the Xinchang gas field in western China were used to test the new method. The results show that the correlation between the synthetic seismic records and the actual seismic records is the best, and the lithofacies matching degree of the inversion is the highest. The results of the conventional multi-point geostatistical inversion are the next best, and the results of the two-point geostatistical inversion are the worst. The results show that the reservoir parameters obtained using the local probability updating of lithofacies method are closer to the actual reservoir parameters. This method is worth popularizing in practical exploration and development. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.3390/en15010299
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: 299
    Subjects:
      – SubjectFull: Lithofacies
        Type: general
      – SubjectFull: Random noise theory
        Type: general
      – SubjectFull: Probability theory
        Type: general
      – SubjectFull: Statistical sampling
        Type: general
      – SubjectFull: Facies
        Type: general
      – SubjectFull: Test methods
        Type: general
      – SubjectFull: Problem solving
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: A Multi-Point Geostatistical Seismic Inversion Method Based on Local Probability Updating of Lithofacies.
        Type: main
  BibRelationships:
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          Name:
            NameFull: Wang, Zhihong
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            NameFull: Chen, Tiansheng
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            NameFull: Hu, Xun
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            NameFull: Wang, Lixin
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            NameFull: Yin, Yanshu
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            – D: 01
              M: 01
              Text: Jan2022
              Type: published
              Y: 2022
          Identifiers:
            – Type: issn-print
              Value: 19961073
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              Value: 15
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              Value: 1
          Titles:
            – TitleFull: Energies (19961073)
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