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The aim of this paper is to assess the use of a recent facies modelling method (Adaptive Plurigaussian Simulation or APS) for facies-uncertainty analysis and sensitivity study in a complex field-development project. This method provides multiple facies realizations that honours all prior data (well, seismic, and geological concept) can be updated when conditioning to dynamical data, using ensemble methods, when production starts. The method is set up as part of an integrated and automated workflow (Fast Model Update or FMU) for reservoir modelling and characterization that is implemented in Statoil. We demonstrate that the use of APS within an FMU workflow allows for fast simulation of facies sensitivities that are reflected in the production profiles.

Knowledge of static properties of reservoirs is a prerequisite to infer its economic value. Conventional reservoir modelling workflows intend to integrate surface seismic reflection data with well log petro-physical relations to generate multiple 3D scenarios of the geological properties. In this study, we used stochastic seismic inversion to improve the acoustic impedance resolution and to better infer its statistical distribution of the porosity of a strongly heterogeneous thighs oil reservoir. Stochastically inverted impedances are then used as secondary data to simulate the strong multi-modal distribution of the porosity. The results show that the complete stochastic approach of the porosity simulation of the tight oil reservoir permits to better represent extreme values and multi-modal behaviour of the porosity distribution. It also permits to improve the resolution of the spatial distribution of the porosity within the reservoir, so as to better infer its uncertainty.