A Bayesian approach to inverse modelling of stratigraphy, part 2: Validation tests

A novel inverse modelling method is applied to the problem of constraining the environmental parameters (e.g. relative sea level, sediment supply) that control stratigraphic architecture. This technique links forward modelling of shallow-marine wave/storm-dominated stratigraphy to a combination of inverse methods formulated in a Bayesian framework. We present a number of examples in which relative sea-level and sediment-supply curves were inferred from synthetic vertical successions of grain size (e.g. wells) and synthetic thickness curves (e.g. seismically derived isopachs) extracted from a forward model simulation. These examples represent different scenarios that are designed to test the impact of data distribution, quantity and quality on the uncertainty of the inferred parameters. The inverse modelling approach successfully reproduces the gross stratigraphic architectures and relative sea level and sediment-supply histories of the synthetic forward model simulation, within the constraints of the modelled data quality. The relative importance of the forcing parameters can be evaluated by their sensitivity and impact on the inverted data. Of equal importance, the inverse results allow complete characterisation of the uncertainties inherent to the stratigraphic modelling tool and to the data quality, quantity and distribution. The numerical scheme also successfully deals with the problem of non-uniqueness of the solution of the inverse problem. These preliminary results suggest that the inverse method is a powerful tool in constraining stratigraphic architecture for hydrocarbon reservoir characterisation and modelling, and it may ultimately provide a process-based geological complement to standard geostatistical tools.