Reservoir parameter estimation with improved particle swarm optimization

Effective reservoir management relies heavily on the accurate prediction of reservoir flow performance over the entire life of the reservoir. Accurate prediction of flow performance is possible only if reservoir flow parameters are known with reasonable accuracy. Consequently, a major challenge in reservoir management is the estimation of reservoir parameters that affect the flow and distribution of reservoir fluids the most. Estimation of distributed reservoir parameters has been done using mainly gradient-based optimization algorithms because these algorithms are relatively faster than global (stochastic) optimization algorithms. However, the gradient-based algorithms are local in nature and thus limited in their search ability. In this paper, we present a local-global optimization method that generates multiple realizations of reservoir parameters at coarse scale. The method involves the use of a local search optimization algorithm to parameterize the model space at a coarse scale followed by a stochastic search for better estimates in the vicinity of the local estimate. At the end of the search, the method produces a distribution of estimates that can be used for uncertainty quantification. To test the effectiveness of the method, the local-global optimization algorithm was applied to a sample reservoir with a known distributed permeability field. Results obtained indicate that the method is able to produce multiple history-matched realizations of the permeability field, some of which are closer to the true reservoir permeability distribution than the estimate obtained from an exhaustive local search.