Increasing the recovery factor of brown fields is one of the key challenge for oil companies. When production is driven by water injection, monitoring flood front movements reservoirs is crucial to optimize field production strategies. Most reservoir characterization and saturation tools provide data at specific locations in the reservoir, with a limited radius of investigation around the wellbore. One promising approach to map and monitor the saturation distribution is to deploy 3D electromagnetic surveys. These methods provide an indirect way to determine the saturation by measuring the spatial distribution of resistivity in the inter-well regions.

One of the main challenges in the interpretation of electromagnetic methods is the uncertainty in our knowledge of the reservoir properties in the inter-well regions. These properties are necessary to derive saturation maps from resistivity measurements. This study introduces a novel approach to interpret electromagnetic surveys, producing robust saturation maps that can guide reservoir management practices. The described workflows apply dynamic simulation and advanced uncertainty quantification. The main uncertain parameter that is considered in the proposed approach is permeability, which is assumed to be a spatial parameter. The probabilistic collocation method was applied to efficiently quantify uncertainty in permeability and its effect on the salinity distribution. The outcome of this method is a polynomial proxy model that can be used to perform statistical analyses using a significantly large sample set with minimal computational cost. The results obtained showed that it is possible to investigate a wide range of uncertainty in a computationally efficient manner, providing more robust saturation maps compared to current practices.

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