Statistical Lobe Hierarchy Constraint in Surface-based Reservoir Modeling

Abstract

Surface-based geomodeling produces realistic and complex stratigraphy of hydrocarbon reservoirs by imitating the underlying physics with depositional rules. However, conventional rules utilized in surface-based models are designed based on qualitative understandings, which are normally implemented with a large number of tuning parameters. The increment of tuning parameters increases workloads in model conditioning and history matching. Moreover, there is no quantitative criterion to validate reproduction of the qualitative rules in model realizations. This work aims to introduce a set of simple random walk based depositional rules for lobate environments such that the number of empirical coefficients and parameters are minimized while the realism of the model is maintained. A quantitative characterization of the qualitative concept of lobe stacking pattern is also defined based on hierarchical clustering analysis. Demonstrations show that realizations of the new model were hierarchically similar to the input lobe sequence.