A subsurface reservoir model is a computer based representation of petrophysical parameters such a porosity, permeability, fluid saturation, etc. Given that direct measurement of these parameters is limited to a few wells it is necessary to extrapolate their distribution. As geology is a first order control on petrophysics, it follows that an understanding of facies and their distribution is central to predicting reservoir quality and architecture. The majority of reservoir modelling systems used for the subsurface are based on correlation of seismically-derived surfaces to define reservoir zones. Well data are then used to define further, sub-seismic scale horizons and determine the zone properties which are represented in grid cells. Understanding the distribution of both sub-seismic surfaces and potential heterogeneous geology between them remains a significant challenge. Furthermore as the typical grid cell size is c. 50-200 m2 it is challenging to incorporate small-scale heterogeneities. It is critical, therefore, to use realistic values for both key stratigraphic horizons and internal facies distributions. Depositional facies is a fundamental control on petrophysics. However, facies scale heterogeneities are not resolvable using current seismic methods, and well data provide little or no data on 3D geometries beyond the well bore. Studies of modern sedimentary events can give some indication of the link between depositional processes and facies distribution (e.g., Kenyon et al., 1995); however preserved depositional architecture is also strongly controlled by changes in accommodation through time (Jervey, 1988). Laboratory-based experiments (e.g., Kneller & Buckee, 2000) and process-based modelling (e.g. Aigner et al., 1989; Peakall et al., 2000) further illustrate the link between depositional mechanism and facies architecture. However, such models are typically on a scale that is far smaller than the typical field and are more applicable to upscaling studies (Nordhal et al., 2005; Ringrose et al., 2005). Outcrop studies have long been employed as a mechanism of studying analogues and understanding petroleum fields (Collinson, 1970; Glennie, 1970; Breed & Grow, 1979). Once the type of depositional system and the accommodation history of a hydrocarbon field are derived from subsurface data, appropriate outcrop analogue(s) can then be identified (e.g. Alexander, 1993). Suitable analogues are those that are geologically comparable to the system that is being studied and also have excellent 3D outcrop exposure over an area that is large enough to capture the scale of heterogeneity required (Clark & Pickering, 1996). Outcrop analogue studies are thus a key way of improving understanding of reservoir facies architecture, geometry, and facies distributions. Outcrop analogue studies have been undertaken both qualitatively and more recently quantitatively. Traditional quantitative studies (e.g., Dreyer et al., 1993; Chapin et al., 1994; Bryant & Flint, 1993; Clark & Pickering, 1996; Reynolds, 1999) have been focused on the collection of outcrop data to populate inter-well reservoir model areas by stochastic, object-based methods (Floris & Peersmann, 2002). However, it can be difficult to extract usable data from traditional outcrop studies, especially when it needs to be integrated with petroleum engineering databases or to be visualized in 3D. Furthermore, outcrops which represent a topographic cut through solid geology are 2D and while rare examples show multiple sections through the solid geology with different orientations, geological expertise is still required to fully understand and interpret the 3D nature of the bodies. Such work may also need geostatistical data …