Reservoir Saturation Mapping from Electromagnetic Geophysical Surveys using Advanced Uncertainty Quantification Methods

Abstract Electromagnetic methods are a promising technology that allows measuring spatial distribution of resistivity in the inter-well region; its deployment has the objective to map and monitor fluid distribution deep into the reservoirs, kilometers away from the surveyed wells. There are many challenges in the interpretation of resistivity distribution in terms of fluid saturation. The management of the uncertainties in the knowledge of reservoir properties in the inter-well regions is very critical to the petrophysical interpretation of the resistivity surveys. Most reservoir characterization and saturation tools provide data at specific locations in the reservoir, with a limited radius of investigation around wellbores, typically cores, production and well logs data. This study introduces a novel approach in interpreting electromagnetic surveys and producing robust saturation maps that can guide reservoir management policies, with the ultimate objective of increasing hydrocarbon recovery. The developed workflow will apply dynamic simulation and advanced uncertainty quantification methods. The uncertain parameter considered is the permeability which is assumed to be spatial uncertainty. Two methods were applied to efficiently quantify uncertainty in the permeability its effect on the salinity distribution. The first method is the probabilistic collocation method (PCM). The PCM is based on the use of polynomial chaos expansion, which is used to develop a polynomial proxy model. The coefficients of this model are determined by performing reservoir simulation using an optimized set of collocation points, which approximate the sample space. The number of these samples that are required are significantly fewer than those required for a typical Monte Carlo or Latin Hypercube Simulation. The second method is the Multi-Level Monte Carlo (MLMC) method, which is based on the use of a combination of multi-level sequentially coarsened (upscaled) grids which shift most of the computational cost to the coarsened grids while minimizing the computational cost of running the simulator on the finer grid level. 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.