Dynamic Validation of a Multi-Point Statistics Model using Extended Well Test Data

Abstract

In Field A, an extended well test carried out over 5 months was used to validate 5 facies models generated with Multi-Point Statistics, each declined into a set of petrophysical models. The geological context of Field A is that of low NTG fluvial deposits which include channel and bar sandstones, alluvial splay siltstones and flood plain shales. Sparse static conditioning data, limited spatial information and naturally complex 3-D channel networks imply that various models can be equi-probable. Multipoint statistics (MPS) approaches were used to model sedimentary bodies in order to ensure the consistency between the model and the geological understanding. Simulation of the conditioned MPS models enabled to reproduce a set of extended well test responses and hence compare the dynamic behaviour of the models against that of the reservoir. Successive comparisons enabled to extract 2 MPS models which matched respectively the early and late time well test data. To obtain a better match over the whole well test data, an engineering-based hybridisation algorithm was used to combine the 2 better-fit models. The successful implementation of this algorithm resulted in promising results and enabled to obtain a quality match with the real well test data.