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International audience; For stratigraphic grids, the traditional method used to model properties does not consider the volume of the cells and ignores the volumetric distortion between physical and depositional space. Therefore, it introduces some biases. A sampling based method allows to solve this problem for variogram-based geostatistics.

International audience; The appraisal stage net-to-gross (NTG) uncertainty of a deepwater turbidite reservoir is assessed using a comprehensive workflow that accounts for uncertainty due to different geological scenarios, incorporates historical information and company expertise through prior probability distributions, and integrates seismic data to correct for the potential bias in NTG estimate due to preferentially drilled wells. Results show that the impact of the geological scenario on NTG uncertainty is larger when the facies geometries are very different. Both the range and shape of the prior distribution impact the posterior NTG probability distributions; the range of the posterior distribution is smaller than that of the prior distribution because of the underlying Bayesian framework. With each additional well, the posterior distributions becomes narrower, indicating that more data reduces uncertainty about the global NTG value. INTRODUCTION The quest for new hydrocarbon reservoirs and technical advances take the industry deeper offshore, opening new domains for exploration and appraisal. In such a context, due to the high cost of reservoir development, a reliable uncertainty assessment of the original hydrocarbon in place (OHIP) is a must to decide on the economic viability of a field. Due to high cost of drilling in offshore environments, wells tend to be sparse and preferentially located in high pay zones. Assessing the uncertainty from such sparse data requires identifying and ranking the major parameters that control the OHIP uncertainty, and defining a sound method to make these parameters variable. Geostatistical simulation methods (Goovaerts, 1997;Chiì es and Delfiner

International audience; Quantifying the proportions and distribution of rock types is a key step in modeling sedimentary formations, since it greatly determines the quality of petrophysical and dynamic flow models. While forward stratigraphic models well reproduce deposition processes, they rely on inversion for data conditioning, which critically raises the empty space problem. Therefore, several stochastic methods have been suggested to simulate proportions and distribution of rock types in reservoirs respecting the observation data while reproducing realistic spatial patterns. However, these methods are always limited by the stationarity decision. We propose a method to generate 3D facies probability cubes which account for well and seismic data, stratigraphic interpretation, sedimentological rules describing the spatial distribution of rock types, and sequence stratigraphy principles. Different probability cubes are computed by integrating various information controlling the lithofacies occurrence, for instance the stratigraphic control of the shoreline migration, by defining linear or inequality constraints at the spatial estimation stage. The generated P-field cubes can then be combined considering the redundancy of the data they express such as the tau model. This methodology is demonstrated on typical synthetic data sets and on a North Sea reservoir.