This paper establishes an error compensation multi-objective optimization model of oil-gas production process for optimizing these production indices, including overall oil production, overall water production and comprehensive energy consumption per ton of oil. In order to reduce the error between the model output and the actual value of comprehensive energy consumption per ton of oil, combining the mechanism model with least squares support vector machine (LS-SVM) error model optimized by Bayesian optimization algorithm (BOA), a hybrid model is established to predict the comprehensive energy consumption, in which the mechanism model is used to describe the overall characteristics of oil-gas production process, and LS-SVM error model is established to compensate the mechanism model error. Then, in order to improve the performance of Pareto non-dominated solutions, an improved non-dominated sorting genetic algorithm-II with multi-strategy improvement (IMS-NSGA-II) is proposed to solve the error compensation multi-objective optimization model. Finally, the effectiveness and superiority of the the proposed optimization method are verified by the experiment results on some stand test problems and the optimization problem for the oil-gas production process in a block of an oil production operation area.

中文翻译：

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基于改进进化算法综合能耗补偿模型的油气生产过程多目标优化

本文建立了油气生产过程的误差补偿多目标优化模型，以优化这些生产指标，包括总产油量、总产水量和吨油综合能耗。为了减小模型输出与吨油综合能耗实际值之间的误差，将机理模型与通过贝叶斯优化算法（BOA）优化的最小二乘支持向量机（LS-SVM）误差模型相结合，得到建立混合模型对综合能耗进行预测，采用机理模型描述油气生产过程的整体特征，建立LS-SVM误差模型对机理模型误差进行补偿。然后，为了提高Pareto非支配解的性能，提出了一种改进的多策略改进非支配排序遗传算法-II(IMS-NSGA-II)来求解误差补偿多目标优化模型。最后，通过对某采油作业区某区块的一些台架试验问题和油气生产过程优化问题的实验结果，验证了所提优化方法的有效性和优越性。