Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4690373 | Sedimentary Geology | 2009 | 11 Pages |
Predicting facies distribution and the stratigraphic architecture of sedimentary basins by process-oriented numerical models is nowadays an essential tool in geologic studies. They constitute a new approach to predict the geologic heterogeneity and the spatial distribution of the diverse facies generated in a depositional system jointly with the distribution of the physical, chemical, and petrophysical characteristics of the sedimentary deposits in a quantitative way. SIMSAFADIM-CLASTIC is a 3D process-based, forward numerical model for the simulation of clastic sediment transport and sedimentation in aquatic systems. It simulates the physical process of clastic transport using the advective, diffusive, and dispersive terms of the transport equation and clastic sediment deposition as a result of a variety of processes. The capabilities of SIMSAFADIM-CLASTIC have been confirmed through the application of the program to a large deep elongated artificial lake, the Camarasa reservoir in the Noguera Pallaresa River, NE of Spain. Simulation results yield sedimentation rates ranging from 0.04 to 0.09 cm·yr− 1 close to the dam, and from 1.73 to 1.63 cm·yr− 1 in the upper reservoir section. The sample experiment results match well with the observed transport pattern linked to the flow system in Camarasa's reservoir near-bottom water layer, which transports more than 50% of the sediment that is supplied to the reservoir. Opening and closure of turbine gates and the basin geometry are the main controlling factors on the fluid flow and depositional pattern in the reservoir, with a more diversified pattern obtained when an open boundary is defined. However, the resulting model also shows some limitations of the program as it does consider a stratified water column that is consistently observed in the reservoir. Refined modeling exercises of the type described in this paper are of potential application to predict and quantify sedimentation patterns allowing the implementation of efficient preservation measures against reservoir siltation.