View in EDS PDF Full Text

Optimized Multi-Layers Inversion Scheme for Azimuthal Resistivity Logging-While-Drilling Based on Supervised Descent Method.

Saved in:
Bibliographic Details
Title: Optimized Multi-Layers Inversion Scheme for Azimuthal Resistivity Logging-While-Drilling Based on Supervised Descent Method.
Authors: Xu, Yongsheng1, Li, Yuehui1 liyuehui@xhu.edu.cn, Zheng, Junyuan1, Sun, Xiangyang2, Hao, Peng2, Ren, Jie1
Source: Progress in Electromagnetics Research C. 2025, Vol. 162, p234-241. 8p.
Subjects: Multilayers, Machine learning, Directional drilling, Hydrocarbon reservoirs, Geophysical well logging
Abstract: Logging-While-Drilling (LWD) azimuthal resistivity measurements deliver critical support for geosteering in complex hydrocarbon reservoirs by acquiring real-time azimuthal responses of formation electrical properties around the borehole; the precision and efficiency of its inversion directly govern the reliability of horizontal well trajectory optimization strategies. Currently, the inversion study of azimuthal resistivity logging with drilling mainly focuses on the simplified three-layer stratigraphic model, and this simple layered model and limited stratigraphic parameter settings have been difficult to adapt to the needs of the increasingly complex geological exploitation. However, inversion of complex multilayer formations (≥ 5 strata) confronts three main challenges: high-dimensional parameterization, attenuated response sensitivity, and noise-impaired accuracy. These constraints compromise field-applicable accuracy thresholds for multilayer stratigraphic inversion. To address the above problems, in this paper, by combining the advantages of traditional inversion methods with machine learning concepts, a new optimized supervised descent inversion method is proposed for azimuthal resistivity LWD in a five-layer formation model. The data-adaptive reconstruction algorithm enhances outer formation response sensitivity. The subsequent integration of multi-matrix fusion with secondary inversion optimization further augments accuracy in field well-log inversion. Numerical simulations and downhole measurements verify the effectiveness of the proposed method, which is a field-deployable real-time inversion algorithm with higher accuracy and stronger noise immunity. [ABSTRACT FROM AUTHOR]
Copyright of Progress in Electromagnetics Research C is the property of Electromagnetics Academy 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 Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 190420599
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Optimized Multi-Layers Inversion Scheme for Azimuthal Resistivity Logging-While-Drilling Based on Supervised Descent Method.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Xu%2C+Yongsheng%22">Xu, Yongsheng</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Li%2C+Yuehui%22">Li, Yuehui</searchLink><relatesTo>1</relatesTo><i> liyuehui@xhu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zheng%2C+Junyuan%22">Zheng, Junyuan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Sun%2C+Xiangyang%22">Sun, Xiangyang</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Hao%2C+Peng%22">Hao, Peng</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Ren%2C+Jie%22">Ren, Jie</searchLink><relatesTo>1</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Progress+in+Electromagnetics+Research+C%22">Progress in Electromagnetics Research C</searchLink>. 2025, Vol. 162, p234-241. 8p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Multilayers%22">Multilayers</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Directional+drilling%22">Directional drilling</searchLink><br /><searchLink fieldCode="DE" term="%22Hydrocarbon+reservoirs%22">Hydrocarbon reservoirs</searchLink><br /><searchLink fieldCode="DE" term="%22Geophysical+well+logging%22">Geophysical well logging</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Logging-While-Drilling (LWD) azimuthal resistivity measurements deliver critical support for geosteering in complex hydrocarbon reservoirs by acquiring real-time azimuthal responses of formation electrical properties around the borehole; the precision and efficiency of its inversion directly govern the reliability of horizontal well trajectory optimization strategies. Currently, the inversion study of azimuthal resistivity logging with drilling mainly focuses on the simplified three-layer stratigraphic model, and this simple layered model and limited stratigraphic parameter settings have been difficult to adapt to the needs of the increasingly complex geological exploitation. However, inversion of complex multilayer formations (≥ 5 strata) confronts three main challenges: high-dimensional parameterization, attenuated response sensitivity, and noise-impaired accuracy. These constraints compromise field-applicable accuracy thresholds for multilayer stratigraphic inversion. To address the above problems, in this paper, by combining the advantages of traditional inversion methods with machine learning concepts, a new optimized supervised descent inversion method is proposed for azimuthal resistivity LWD in a five-layer formation model. The data-adaptive reconstruction algorithm enhances outer formation response sensitivity. The subsequent integration of multi-matrix fusion with secondary inversion optimization further augments accuracy in field well-log inversion. Numerical simulations and downhole measurements verify the effectiveness of the proposed method, which is a field-deployable real-time inversion algorithm with higher accuracy and stronger noise immunity. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Progress in Electromagnetics Research C is the property of Electromagnetics Academy 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=190420599
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.2528/PIERC25083001
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 234
    Subjects:
      – SubjectFull: Multilayers
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Directional drilling
        Type: general
      – SubjectFull: Hydrocarbon reservoirs
        Type: general
      – SubjectFull: Geophysical well logging
        Type: general
    Titles:
      – TitleFull: Optimized Multi-Layers Inversion Scheme for Azimuthal Resistivity Logging-While-Drilling Based on Supervised Descent Method.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Xu, Yongsheng
      – PersonEntity:
          Name:
            NameFull: Li, Yuehui
      – PersonEntity:
          Name:
            NameFull: Zheng, Junyuan
      – PersonEntity:
          Name:
            NameFull: Sun, Xiangyang
      – PersonEntity:
          Name:
            NameFull: Hao, Peng
      – PersonEntity:
          Name:
            NameFull: Ren, Jie
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Text: 2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 19378718
          Numbering:
            – Type: volume
              Value: 162
          Titles:
            – TitleFull: Progress in Electromagnetics Research C
              Type: main
ResultId 1