Efficient Ensemble-Based Closed-Loop Production Optimization

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Gaussian Processes for history-matching: application to an unconventional gas reservoir (2017, background): “data assimilation methods can be defined in a minimization context [6], they are generally classified as uncertainty quantification techniques [7, 8].”
Theoretical connections between optimization algorithms based on an approximate gradient (2013, methodology): “The authors of [7] do not comment on the generation of CU .”
Model-reduced gradient-based history matching (2011, methodology): “A particularly efficient class of history matching methods are gradient-based history matching algorithms, where the gradients of the objective function with respect to the model parameters are calculated by solving an adjoint, or co-state, problem as introduced by Courant and Hilbert (1953). Those ”
A Stochastic Simplex Approximate Gradient (StoSAG) for optimization under uncertainty (2017, background): “[13] in 2009, implicitly produces a smoothed version of the approximate gradient because it can be interpreted as a gradient pre-multiplied with the covariance matrix used in generating the random controls.”
Gradient-based multi-objective optimization with applications to waterflooding optimization (2016, background): “One typically then does robust optimization [4, 5, 24] where we maximize the expectation of NPV over an ensemble of reservoir models.”