1887
Volume 33, Issue 9
  • ISSN: 0263-5046
  • E-ISSN: 1365-2397

Abstract

Recovering the remaining and bypassed hydrocarbons in mature areas requires well-informed decision-making supported by good data. A great deal of seismic exploration has taken place within the North Sea, using a variety of acquisition configurations including the latest broadband solutions. Most surveys in this mature area have been processed and reprocessed multiple times as techniques have evolved and, every so often, significant advances in technology warrant the application of these new approaches in a wholesale way. The Central North Sea (CNS) still contains many oppor¬tunities and is notoriously challenging to image with seismic. Fuelled by the need for greater accuracy of deep imaging and structural interpretation, for both prospect discovery and field development, there has been a steady increase in the number of pre-stack depth migration (PSDM) projects. The majority of these projects have focused on small areas, primarily restricted by the high work effort involved in integrating all available data (wells, horizons, vertical seismic profiles, etc.) into the velocity model, combined with the lengthy timescales required to update the model using traditional, interpretation-heavy workflows and tomography methods. Recent advances in multi-layer tomography (Guillaume et al., 2012) now allow us to complete complex PSDM projects within a significantly reduced timescale while at the same time achieving improved image quality. In addition, advances in deghosting and demultiple processing technolo¬gies have enabled us to extract even more value from vintage conventional flat-towed streamer datasets. We describe how these technologies have been applied to integrate 37 acquisi¬tion phases covering more than 35,000 km2 in the CNS to generate a unique and contiguous high-quality broadband PSDM dataset which sets new standards for regional datasets in mature basins.

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/content/journals/0.3997/1365-2397.33.9.82616
2015-09-01
2024-04-24
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  • Article Type: Research Article
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