Quantifying tabularity of turbidite beds and its relationship to the inferred degree of basin confinement

Tabular beds and sheet-like deposits in deep-water systems have been the subject of much research attention; they can form high quality hydrocarbon reservoirs, owing to their excellent lateral continuity and predictable geometry. Additionally, deposit tabularity is a piece of evidence used to infer flow confinement in ancient systems and thus to evaluate the suitability of outcrop datasets as reservoir analogues. However, the quantification of tabularity is rarely attempted and a consistent definition on how to describe it quantitatively is lacking. For this study, published data from eighteen well-constrained ancient turbidite systems in outcrop were analysed. A simple and novel methodology for the quantitative calculation of tabularity along a transect from log panels and photo panels was devised, based on: a) subdividing beds into two groups based on their thickness, b) calculating the percentage of beds continuous across a fixed window (500m) and c) calculating the rate of thinning for the continuous beds within the same window. Calculations obtained from multiple locations within individual systems enable the investigation of proximal to distal, and axial to lateral changes in tabularity to be captured, and therefore permits the evaluation of tabularity in three-dimensions. A comparison between tabularity of the considered systems and their inferred degree of basin confinement shows that in the confined systems >90% of beds are continuous over 500m compared to <40% for the two unconfined systems studied. In addition, different bed types were compared: hybrid event bed thinning rates are shown to be up to three times those of classical turbidites. This methodology provides a new tool to compare tabularity within and between systems quantitatively. It is hoped that the quantitative determination of tabularity will become a common workflow when describing ancient turbidite systems. It is suggested that this approach will enhance the value of outcrop data to inform models capturing the architecture of systems analogous to subsurface hydrocarbon reservoirs.