Dimensionless Ensemble Smoother With Multiple Data assimilation applied on an Inverse Problem of a multilayer reservoir with a damaged zone

The ES-MDA has been used extensively concerning inverse problems of oil reservoirs, using Bayesian statistics as the core.

Important properties such as permeability, skin zone radius, and skin zone permeability are estimated from historical reservoir data using this set-based method. In this thesis, the pressure measured in the well during an injectivity test was calculated using an analytical approach of a multilayer reservoir, with skin zone, using the Laplace Transform. Stehfest's algorithm was used to invert the data to the real field. Furthermore, by using this approach, we were able to easily obtain the flow rate in each layer as a new data to be considered in the ES-MDA, enriching the estimation of the targeted data. As we use flow rate and pressure as input data in the ES-MDA, the difference in orders of magnitude mustn't influence our estimates, and that is why we chose to use the ES-MDA in the dimensionless form. Aiming at a greater precision of our estimates, we used an algorithm to optimize the ES-MDA inflation factors.