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Sediment partitioning, continental slopes and base-of-slope systemsNormal access

Authors: B.E. Prather, C. O'Byrne, C. Pirmez and Z. Sylvester
Journal name: Basin Research
Issue: Vol 29, No 3, June 2017 pp. 394 - 416
DOI: 10.1111/bre.12190
Organisations: Wiley
Language: English
Info: Article, PDF ( 10.79Mb )

Summary:
Deciphering the role slope topography plays in partitioning sediment on siliciclastic continental slope and base-of-slope systems helps our understanding of slope depositional processes in significant ways: (1) by validation of large-scale depositional process models for continental margins, (2) by validation of numerical basin-scale stratigraphic forward models used to test and deploy source-tosink (S2S) concepts and (3) by creating models for setting reservoir presence and quality expectations in frontier areas poorly constrained by wells and seismic. A global database consisting of >700 km of drilled stratigraphy provide empirical rock data lacking from most S2S studies. Analysis of calibrated seismic stratigraphic units characterised using the contextual framework laid out in this paper show that both gross depositional environments (GDEs) and sand content occur across slope profiles in systematic ways. The challenge in using these observations to quantify reservoir risk and uncertainty lies with relating the observations to depositional processes that can be used to characterise frontier basins that lack calibration. Depositional process-based understanding encoded in 3D stratigraphic forward models (SFM) can simulate both lithologies and GDEs providing broad predictions for exploration at the scale of an entire basin or slope system. Stratigraphic forward models allow the integration of S2S understanding and provide a framework for testing sediment-partitioning hypotheses in frontier settings. Valid S2S models must balance sediment yield from the source catchments with sinks, and be consistent with basin specific observations. The proportions of GDEs across the slope provide additional validation criteria to ensure the models are plausible.

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