Comparison of sequential indicator simulation, object modelling and multiple-point statistics in reproducing channel geometries and continuity in 2D with two different spaced conditional datasets

Fluvial sediments with multi-scale channels are difficult to model using classical two-point statistical methods, e.g. sequential indicator simulation (SIS) or object-based modelling (ObjM). Multiple-point statistics (MPS) has been used to generate facies, fracture and porosity distributions based on pattern statistics derived from training datasets. However, the ability of these three methods to reproduce channel geometry and continuity is not clear, especially when using differently spaced conditional data. This paper presents a case study to compare the application of these three methods in reproducing channels from a section of Amazon River based on two differently spaced conditional data sets. Results show that: the reproduction accuracy is similar between MPS and SIS; MPS provides the most connected channel facies (or most channel continuity) as compared to SIS and ObjM; and using a hand-drawn facies based on the sampling points yield a similar accuracy to that achieved by using the reality facies distribution as the training image. Finally, we conclude that the application of MPS does not significantly increase the reproduction accuracy when compared to SIS channel models; however, MPS can generate realistic models with respect to channel geometry and continuity.