--> Channel Belt Rugosity in Reservoir Characterization

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Channel Belt Rugosity in Reservoir Characterization

Abstract

Fluvial systems are typically classified based on river channel morphology, with meandering and braided river patterns being the two most common end-member types. However, seismic data commonly cannot resolve channel morphologies, only the much larger channel belt. This creates challenges when applying channel classifications to subsurface reservoirs. Log and core data allow the interpretation of bar patterns within river channels that aid in the interpretation of larger channel belts. Typically, downstream accretion results in fewer sand/mud interbeds and is often associated with a braided channel morphology, whereas lateral accretion leads to abundant sand/mud interbeds and associations with a single thread sinuous channel. Still, core and log data do not allow for a direct and confident interpretation of braided or sinuous channel morphology. River channels and channel belts can easily be identified using satellite imagery. Channel-belt margins vary in smoothness depending on the dominant style of bar-form migration: lateral or downstream. We term this smoothness of the channel-belt margin, Rugosity. Rugosity is used in marine science to characterize seafloor habitats. Rugosity is herein used to describe how dissimilar the opposing sides of a fluvial channel belt are in planview. Rugosity (fr) is a measure of small-scale variations or amplitude in the height of a surface, fr = Ar/Ag, where Ar is the actual planform area and Ag is a geometric approximation of the channel-belt area. We found that increasing lateral accretion of barforms (caused by increased channel sinuosity) leads to an increase in channel-belt rugosity. Therefore rugosity could be a proxy for interpreting the relative degree of lateral vs. downstream accretion within channel belts. This is a potentially powerful tool for resource estimation and extraction, as it may improve predictions of internal heterogeneity using seismic data.