Unsupervised clustering of frequently repeated 4D seismic data for delineation of CO2 plume development

Boshara M. Arshin Sukar*, Colin MacBeth

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

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Abstract

Frequently repeated 4D seismic has inspired new ways to interpret time-lapse changes. One such application is well2seis, in which 4D signals are quantitatively correlated to well production to create a localized signal that reveals spatial connectivity and fluid pathways in the reservoir. However, the causal relation between multiple seismic vintages and production data in well2seis is strongly dependent on the availability of cumulative fluid volumes that correspond to multiple acquisition periods. Also, it is difficult to disentangle overlapping responses caused by fluctuations from multiple wells. In this study, we propose a machine learning implementation of this technique (well2seisML) to resolve these issues. The new solution is applied to CO2 plume evolution during injection in the Sleipner field. This allows us to optimally integrate the data from injected volumes with the repeat seismic data.

Original languageEnglish
Pages (from-to)994-998
Number of pages5
JournalSEG Technical Program Expanded Abstracts
Volume2023
DOIs
Publication statusPublished - 14 Dec 2023
Event3rd International Meeting for Applied Geoscience and Energy 2023 - Houston, United States
Duration: 28 Aug 20231 Sept 2023

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Geophysics

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