Abstract
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.
Highlights
Oil and gas resources are the blood of national development
In order to cope with the difficult situation of solving the multi-cycle full process integrated mixed-integer nonlinear programming (MINLP) model, this paper further investigates the batch decomposition strategy based on Lagrangian relaxation algorithm to improve the solving efficiency
The red line is the trend of solving gaps solved by Lagrange relaxation (LR) algorithm for MINLP model
Summary
Oil and gas resources are the blood of national development. Oil and gas production plays an extremely important role in promoting industrial development and social progress. A multi-period mixed integer nonlinear programming (MINLP) model was proposed to minimize the total operation cost, considering well production state, polymer flooding, energy consumption, platform inventory and flow assurance. By solving this integrated model, each well’s working state, flow rates and chemicals injection rates can be optimally determined [18]. In order to cope with the difficult situation of solving the multi-cycle full process integrated MINLP model, this paper further investigates the batch decomposition strategy based on Lagrangian relaxation algorithm to improve the solving efficiency.
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