Tidal estuarine basins show a highly heterogeneous distribution of sand and mud sediment. Sand is concentrated by highenergy currents in tidal channels and creeks. Dissipation of tidal energy along the branching channel network and onto adjacent tidal flats causes settling of mud out of suspension. Also, the fine-grained sediment settles on sand during low current-energy slackwater periods in a tidal cycle. Factors such as basin morphology, strength and dissipation pattern of the tidal currents, and vegetation further condition the distribution of sand and mud. This results in a complex spatial distribution of lithofacies which is difficult to model and predict.Adequate modelling of the spatial arrangement of the sandprone parts is especially relevant in hydrocarbon reservoirs in this sedimentary environment, such as, e.g., the Tilje Formation (Pliensbachian, Early Jurassic) reservoirs in the Haltenbanken area offshore mid-Norway (Dreyer, 1992;Donselaar & Geel, 2003;Martinius et al., 2005), the Cook Formation (Early Jurassic) in the Oseberg Field, northern North Sea, offshore Norway (Donselaar et al., 2006), and The Lower Graben Formation (Late Jurassic) in the F3-FB Field, offshore the Netherlands (Wong, 2007). Well spacing is usually too large to capture all spatial sediment heterogeneity and to construct a unique, deterministic reservoir model. Instead, object-based stochastic modelling techniques are applied to to generate multiple realisations of the spatial distribution to assess the uncertainty in sand-distribution and connectivity (Dubrule & Damsleth, 2001
AbstractObject-based stochastic modelling techniques are routinely employed to generate multiple realisations of the spatial distribution of sediment properties in settings where data density is insufficient to construct a unique deterministic facies architecture model. Challenge is to limit the wide range of possible outcomes of the stochastic model. Ideally, this is done by direct validation with the 'real-world' sediment distribution. In a reservoir setting this is impossible because of the limited data density in the wide-spaced wells. In this paper this uncertainty is overcome by using size, shape and facies distributions of tidal channel and tidal flat sand bodies in a highly data-constrained lithofacies architecture model as input for the object-based stochastic model. The lithofacies architecture model was constructed from a densely perforated (Cone Penetration Tests and cored boreholes) tidal estuarine succession of the Holocene Holland Tidal Basin in the Netherlands. The sensitivity of the stochastic model to the input parameters was analysed with the use of varying tidal channel width and thickness values and calculating the connected sand volume per well for the different scenarios. The results indicated that for a small well drainage radius the difference in drainable volumes between the narrowest and the widest channel scenarios is large, and that for a large well drainage radius the tidal channel width hardly influenced the drainable vo...