A Lagrange Relaxation Based Decomposition Algorithm for Large-Scale Offshore Oil Production Planning Optimization
A Lagrange Relaxation Based Decomposition Algorithm for Large-Scale Offshore Oil Production Planning Optimization
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DOI：
10.3390/pr9071257
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发表时间：
2021-07
期刊：
影响因子：
3.5
通讯作者：
Xiaoyong Gao;Yue Zhao;Yuhong Wang;Xin Zuo;Tao Chen
中科院分区：
文献类型：
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作者：
Xiaoyong Gao;Yue Zhao;Yuhong Wang;Xin Zuo;Tao Chen
本文提出了一种新的基于拉格朗日松弛的分解算法，用于海洋石油综合生产计划优化。在我们以前的研究中（Gao et al. Computers and Chemical Engineering，2020，133，106674），提出了一个同时考虑井作业和流动保证的多周期混合整数非线性规划（MINLP）模型。然而，由于问题的大规模性质，即，由于威尔斯油井数量多、规划时间周期长，使得优化问题很难在合理的时间内得到满意的解。作为一种有效的方法，基于拉格朗日松弛的分解算法可以提供更紧凑的边界，从而导致更小的对偶间隙。具体地说，拉格朗日乘子被引入到放松耦合约束的多批单元，从而导致一些适度规模的子问题。此外，对偶问题的迭代构造。其结果是，原来的集成大规模模型被分解成几个单批子问题，并同时解决了商业求解器。计算结果表明，所提出的方法可以减少求解时间高达43%，甚至更多。同时，规划结果与原模型得到的结果接近。而且，问题规模越大，所提出的LR算法比原始模型更好。
In this paper, a new Lagrange relaxation based decomposition algorithm for the integrated offshore oil production planning optimization is presented. In our previous study (Gao et al. Computers and Chemical Engineering, 2020, 133, 106674), a multiperiod mixed-integer nonlinear programming (MINLP) model considering both well operation and flow assurance simultaneously had been proposed. However, due to the large-scale nature of the problem, i.e., too many oil wells and long planning time cycle, the optimization problem makes it difficult to get a satisfactory solution in a reasonable time. As an effective method, Lagrange relaxation based decomposition algorithms can provide more compact bounds and thus result in a smaller duality gap. Specifically, Lagrange multiplier is introduced to relax coupling constraints of multi-batch units and thus some moderate scale sub-problems result. Moreover, dual problem is constructed for iteration. As a result, the original integrated large-scale model is decomposed into several single-batch subproblems and solved simultaneously by commercial solvers. Computational results show that the proposed method can reduce the solving time up to 43% or even more. Meanwhile, the planning results are close to those obtained by the original model. Moreover, the larger the problem size, the better the proposed LR algorithm is than the original model.