首页|A novel self-adaptive multi-fidelity surrogate-assisted multi-objective evolutionary algorithm for simulation-based production optimization
A novel self-adaptive multi-fidelity surrogate-assisted multi-objective evolutionary algorithm for simulation-based production optimization
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NSTL
Elsevier
Multi-objective production optimization (MOPO) which aims at finding an optimal well control scheme is regarded as a win-win strategy for improving economic and production gains. Generally, the MOPO processes involve computationally expensive numerical simulations and high-dimensional decision variables which limit their application to actual reservoir optimization problems. Surrogate-assisted multi-objective optimization method which uses a simple yet efficient approximation model shows huge potential in solving production optimization problems since it lessens the computational burden by decreasing the use of computationally expensive numerical simulations. In this paper, a novel self-adaptive multi-fidelity surrogate-assisted (SAMFS) multi-objective production optimization algorithm (SAMFS-MOPO) is proposed to reduce the computational burden and enhance the accuracy of the surrogate model. A similar method has been applied to well spacing optimization but the uniqueness of this method is that two fidelity samples are used to establish a multi-fidelity (MF) surrogate model, while i-updating and g-updating strategies are used to renew the MF surrogate model during the optimization process so as to improve its accuracy and reduce the computational burden. To the best of our knowledge, this is the first time a self-adaptive multi-fidelity surrogate model is used for production optimization. Furthermore, three classic multi-objective benchmark problems and two reservoirs with different complexities were applied to illustrate the effectiveness and accuracy of the proposed SAMFS-MOPO method. It was found that the SAMFS-MOPO method had superior performance in convergence, diversity, and efficiency than other conventional methods.
Multi-fidelity surrogate modelSelf-adaptive updating strategyMulti-objective optimizationSimulation-based production optimizationWELL PLACEMENTGENETIC ALGORITHMMODELPREDICTIONDESIGN
Wang, Lian、Yao, Yuedong、Zhang, Tao、Zhao, Guoxiang、Lai, Fengpeng、Adenutsi, Caspar Daniel
NSTL
Elsevier
