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Volume 39 Number 12
  • ISSN: 0263-5046
  • E-ISSN: 1365-2397

Abstract

Summary

Because they allow us to integrate the information content contained in multiple seismic attribute volumes, machine learning techniques hold significant promise in the identification and delineation of heterogeneous 3D seismic facies. However, considerable care must be taken in choosing not only the appropriate, but also in their scaling. Sometimes such exercises are carried out mechanically, resulting in compromised interpretations and discouraging results. We examine some of the more well-established unsupervised machine learning techniques such as principal component analysis (PCA) and clustering, as well as some less common clustering techniques like independent component analysis (ICA), self-organizing mapping (SOM), and generative topographic mapping (GTM) as applied to a seismic data volume from the southern Norwegian North Sea. We find that the machine learning methods can provide increased vertical and spatial resolution. However, machine learning is also good at enhancing noise and artifacts. For this reason, the interpreter needs to ensure the data are adequately conditioned, the assumptions on which some of the techniques being applied are based are met, and finally, the most appropriate technique among those discussed in this paper is utilized.

EAGE Publications BV
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/content/journals/10.3997/1365-2397.fb2021089
2021-12-01
2024-04-25

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http://instance.metastore.ingenta.com/content/journals/10.3997/1365-2397.fb2021089
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Seismic characterization of the Middle Jurassic Hugin sandstone reservoir in the southern Norwegian North Sea with unsupervised machine learning applications for facies classification
First Break 39, 35 (2021); https://doi.org/10.3997/1365-2397.fb2021089
/content/journals/10.3997/1365-2397.fb2021089
/content/journals/10.3997/1365-2397.fb2021089
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  • Article Type: Research Article

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