Geomodelling of carbonate mounds using two-point and multipoint statistics

Abstract

Abstract The Late Carboniferous strata exposed in the Sacramento Mountains in Southern New Mexico, USA, have long been considered classic exposures documenting reciprocal high-frequency mixed carbonate–siliciclastic cyclicity and shelf-edge algal-mound growth. The growth style and internal architecture of these phylloid algae mounds depend on their position on the shelf and are controlled by potential accommodation space, depth of the photic zone and hydrodynamic energy. The combination of these parameters results in a laterally variable amount of reworked phylloid algae debris and in-situ mound core facies along the depositional profile. This variable architecture can be observed on the outcrop and results in a complex distribution of these two lithofacies in three dimensions that is challenging to reproduce in a 3D geocellular model. Two geostatistical estimation algorithms are used to stochastically model carbonate buildups: surface-based and multipoint statistics (MPS)-based. The surface-based model uses two-point statistics and is built by first recreating the overall geometry of the mound and then reproducing the internal architecture using indicator Gaussian simulation, but requires strong secondary trend data to reproduce the correct facies architecture. The MPS model successfully recreates both the geometry and internal architecture of the mound, but requires a complicated training image and complex multigrid simulation that would be hard to implement in subsurface. This comparison demonstrates that modelling carbonate buildup geometry and internal architecture is not trivial and requires complex workflow with secondary trends. These secondary trends require a significant amount of prior knowledge that is easily extracted from outcrop observations, but would be difficult to assess in subsurface data.