Effective development of the Carboniferous Barren Red Measures in the Silverpit Basin of the U.K. Southern North Sea, is reliant on a robust correlation framework. This complex, low net:gross, fluvial reservoir is interpreted as being deposited as discrete, low sinuosity, braidplain channel bodies within a floodplain shale. Production history over the last 2 years indicates significant vertical and lateral variability of reservoir architecture, and drives the requirement for improved reservoir performance prediction. Biostratigraphic markers are sparse in the Barren Red Measures and previous attempts at defining reliable stratigraphic subdivisions were based on the identification of potential ‘flooding surfaces’ derived from the correlation of Gamma Ray (GR) peaks between wells.

Geochemical analysis has enabled the generation of a robust, 5-zone chemostratigraphic correlation framework. Each zone defined broadly equivalent packages of strata, recording changes in the basin wide hydrology of the depositional system, and as a result provided a reliable, stratigraphic subdivision. Through analysis of reservoir parameters, and production data, the regional palaeogeographic model was refined, and an improved understanding of the reservoir architecture controls on hydrocarbon flow were achieved.

Incorporation of detailed core analysis resulted in the interpretation of pedofacies in each stratigraphic unit. Sediment maturity was described in terms of the degree of bio/pedoturbation of the original sediment fabric and a link to aggradation rates and reservoir architecture proposed. The cyclic nature of the reservoir was analysed and direct links to the geochemical subdivisions identified. This supported the theory of basin wide hydrological changes fuelling the architectural variations in the reservoir. The integration of production data into the geological model provided support for the formulated theories on reservoir architecture.

Through this integration of geochemical correlation techniques, analysis of pedofacies, cyclicity and production data, it was possible to redefine the geological model and derive a robust framework on which to base both static and dynamic reservoir modeling. This in turn enables improved reservoir performance prediction.

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