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A Novel Surrogate-Assisted Multi-objective Optimization Method for Well Control Parameters Based on Tri-Training

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Abstract

Multi-objective well control optimization, which considers simultaneous optimization of more than one objective, has become an essential tool for efficient intelligent decision-making about well control parameters. In general, multi-objective well control parameter optimization involves numerical simulation, which is computationally expensive. Surrogate-assisted methods that use a simple yet rigorous approximation model have shown great potential in reducing the need for expensive numerical simulations. In order to strike a balance between reducing computational burden and enhancing accuracy of a surrogate model, we introduce here a semi-supervised learning method called tri-training surrogate-assisted multi-objective optimization (MOO–TTSA), which is based on radial basis function network (RBFN) and non-dominated sorting genetic algorithm-II (NSGA-II) to assist the optimization process. The uniqueness of this method is that a tri-training strategy is applied to enrich the training data set by selecting solutions with high-confidence fitness to renew the surrogate model at each generation so as to obtain better accuracy and lessen the computational burden. In this way, the number of simulation runs is reduced while the accuracy of the surrogate is guaranteed. To the best of our knowledge, this is the first time that a combination of tri-training technique and MOO is used for reservoir well control problems. The MOO–TTSA method was applied to two benchmark problems and two synthetic reservoirs. The results showed that the MOO–TTSA method reduced the number of simulation runs on the two reservoirs, one for about 160 times and the other for 50 times. Besides, the hypervolumes of both the benchmark and reservoir problems of MOO–TTSA were similar with those of the simulation-based NSGA-II method (NSGA-II), but superior to those of the single static surrogate model-based NSGA-II method (RBFN–NSGA-II). It was shown that the MOO–TTSA method was more efficient, superior and converged faster compared to the other conventional optimization models.

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Acknowledgments

The authors would like to acknowledge financial support from the National Basic Research Program of China (2015CB250900) and the reservoir numerical simulation software provided by MRST.

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Authors and Affiliations

  1. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, 102249, China

    Lian Wang, Yuedong Yao & Guoxiang Zhao

  2. Changqing Downhole Technology Company, CNPC Chuanqing Drilling Engineering Co., Ltd., Xi’an, 710018, China

    Kongjie Wang

  3. Reservoir Simulation Laboratory, Department of Petroleum Engineering, Faculty of Civil and Geo-Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

    Caspar Daniel Adenutsi

  4. School of Energy Resources, China University of Geosciences, Beijing, 100083, China

    Fengpeng Lai

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  1. Lian Wang
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  2. Yuedong Yao
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  4. Caspar Daniel Adenutsi
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  5. Guoxiang Zhao
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Correspondence to Lian Wang or Yuedong Yao.

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Wang, L., Yao, Y., Wang, K. et al. A Novel Surrogate-Assisted Multi-objective Optimization Method for Well Control Parameters Based on Tri-Training. Nat Resour Res 30, 4825–4841 (2021). https://doi.org/10.1007/s11053-021-09948-9

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