Uncertainty quantification of reservoir performance predictions using a stochastic optimization algorithm

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Theoretical connections between optimization algorithms based on an approximate gradient (2013, methodology): “At iteration k + 1, the SGSD or G-SPSA algorithm of [20] utilizes a search direction, dk , obtained by averaging Ne individual G-SPSA gradients, where each G-SPSA gradient is obtained using a perturbation vector, û , sampled from a normal distribution with mean equal to u and covariance matrix, CU ,”