The Provenance of Triassic Continental Sandstones from the Beryl Field, Northern North Sea: Mineralogical, Geochemical, and Sedimentological Constraints

A geochemical study of the heavy-mineral suite in Triassic strata from two wells in the Beryl Embayment was undertaken in order to identify changes in provenance through the Lewis Formation. Detailed provenance studies also allow the reconstruction of paleodrainage patterns and aid in predicting facies distributions. The heavy-mineral assemblage in the Lewis Formation is dominated by almandine-pyrope garnets, which are typically derived from pelitic metasediments, together with zircon, apatite, tourmaline, and rutile, some of which are rich in niobium (Nb). These latter phases could have been sourced either from metasediments or granitic protoliths, although Nb-rich rutiles have a more restricted paragenesis, being associated with alkaline plutonic rocks and granite pegmatites. In the uppermost members of the Lewis Formation influxes of more exotic provenance-specific heavy-minerals such as Cr-spinel, staurolite, and chloritoid become more common. The chemistry of the detrital Cr-spinel implies that it was derived from an ophiolitic source. Gross grain size, and facies changes in the Embayment suggest a NW to SE dispersal of detritus. Although hydraulic sorting of the heavy-mineral suite between relatively distal and proximal locations has resulted in subtle variations in whole-rock trace-element geochemical signatures, the complete overlap in the geochemical data from distal and proximal wells requires that the sediments were essentially derived from a single source area. The combination of mineralogical, sedimentological, and geochemical data implies that the dominant source for these sediments was the Devonian Old Red Sandstone (ORS), the Shetland ophiolite and the underlying Dalradian metasedimentary rocks of the Shetland Isles and the East Shetland Platform. Provenance studies such as this are central to the reconstruction of paleo-drainage patterns, which in turn lead to improved prediction of potential reservoir quality, distribution, and connectivity.