Low-angle detachment faults and thrust-sheet top basins are common features in foreland basins. However, in stratigraphic analysis their in£uence on sequence architecture is commonly neglected. Usually, only eustatic sea level and changing £exural subsidence are accounted for, and when deformation is considered, the emphasis is on the generation of local thrust-£ank unconformities. This study analyses the e¡ects of detachment angle and repetitive detachment activation on stratigraphic stacking patterns in a large thrust-sheet top basin by applying a three-dimensional numerical model.Model experiments show that displacement over low-angle faults (2^61) at moderate rates ($5.0 m kyr À 1 ) results in a vertical uplift component su⁄cient to counteract the background £exural subsidence rate. Consequently, the basin-wide accommodation space is reduced, £uvio -deltaic systems carried by the thrust-sheet prograde and part of the sediment supply is spilled over towards adjacent basins.The intensity of the forced regression and the interconnectedness of £uvial sheet sandstones increases with the dip angle of the detachment fault or rate of displacement. In addition, the delta plain is susceptible to the formation of incised valleys during eustatic falls because these events are less compensated by regional £exural subsidence, than they would be in the absence of fault displacement.Correspondence and Present Address: Quintijn Clevis, Department of Geological Sciences, CIRES,
We evaluated the relationship between geological parameters and the flow behaviour of channelized reservoirs with the aid of an experimental design approach. The geological parameters included geometrical properties, such as channel dimensions and sinuosity, petrophysical parameters, such as permeability and net-to-gross ratio, and a derived property: connectivity. The reservoir flow behaviour was characterized with various metrics based on simulated production data generated with the aid of reservoir flow simulations. In the first part of our study, we found a weak correlation between the geometrical parameters and the reservoir flow behaviour, whereas we found a strong correlation between connectivity and the flow behaviour. In the second part we demonstrate how to use the strong correlation between connectivity and flow behaviour to make a selection of models with similar production response from a large ensemble of reservoir model realizations.
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