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Multi-objective optimization for oil-gas production process based on compensation model of comprehensive energy consumption using improved evolutionary algorithm

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ENERGY EXPLORATION & EXPLOITATION 2021年第1期39卷 273-298页

作者： Liu, Tan Yuan, Qinyun Wang, Lina Wang, Yonggang Zhang, Nannan Shenyang Agr Univ Coll Informat & Elect Engn Shenyang Peoples R China Liaoning Engn Res Control Informat Technol Agr Shenyang Peoples R China China Jiliang Univ Coll Mech & Elect Engn Hangzhou Peoples R China

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.

关键词： Oil-gas production process ls-svm error model Bayesian optimization algorithm error compensation NSGA-II with multi-strategy improvement (IMS-NSGA-II)

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Adaptive Real-Time Prediction for Oil Production Rate Considering model Parameter Uncertainties

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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2021年第9期54卷 473-485页

作者： Liu, Tan Yuan, Qingyun Wang, Lina Wang, Yonggang Shenyang Agr Univ Coll Informat & Elect Engn Shenyang 110866 Liaoning Peoples R China Liaoning Engn Res Control Informat Technol Agr Shenyang 110866 Liaoning Peoples R China China Jiliang Univ Hangzhou 310018 Zhejiang Peoples R China

In the oil and gas production process, the online prediction of the oil-well production rate is an important task, that cannot only directly reflect the liquid supply capability of oil wells, but also guide the optimal control of the oil and gas production processes. However, traditional prediction methods have certain limitations in terms of accuracy and real-time properties. Therefore, to achieve an accurate prediction of the oil production rate, an adaptive integrated modeling method with a higher prediction accuracy and self-adaptability is proposed in this paper. With this method, a nonlinear mechanism model of the oil production rate is first established by analyzing the oil and gas production process and considering the nonlinear characteristics of the reservoir and multiphase flow in the wells. To reduce the influence of model parameter uncertainty and improve the prediction accuracy of the mechanism model, the least squares support vector machine (ls-svm) method is then used to establish the error model for compensating the deviation in the mechanism model output. Moreover, to improve the adaptability of the model, an online correction strategy including a short-term correction of the ls-svm and long-term correction of the mechanism model is proposed. Finally, through a simulation of the actual oil and gas production process in the oil production area, the results demonstrate that the proposed modeling method can not only improve the model prediction accuracy but also the model generalization, laying a solid foundation for the implementation of optimal control in the oil and gas production process.

关键词： Oil Production Rate Mechanism model ls-svm error model Hybrid Predicting model model Correction Strategy

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