A novel particle swarm optimization (PSO) method for discrete parameters and its hybridized algorithm with multi-point geostatistics are presented. This stochastic algorithm is designed for complex geological models, which often require discrete facies modeling before simulating continuous reservoir properties. In this paper, we first develop a new PSO method for discrete parameters (Pro-DPSO) where particles move in the probability mass function (pmf) space instead of the parameter space. Then Pro-DPSO is hybridized with the single normal equation simulation algorithm (SNESIM), one of the popular multipoint geostatistics algorithms, to ensure the prior geological features. This hybridized algorithm (Pro-DPSO-SNESIM) is evaluated on a synthetic example of seismic inversion, and compared with a Markov chain Monte Carlo (McMC) method. The results show that the new algorithm generates multiple optimized models with the convergence rate much faster than the McMC method.

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