基于EMD-LSTM的集输立管内稳态及瞬态工况预测  

Forecasting the Conditions of Steady State and Transient State in Pipeline-riser Based on EMD-LSTM

作　　者：付吉强 邹遂丰[1] 孙杰 徐强[1] 赵向远 郭烈锦[1] FU Jiqiang;ZOU Suifeng;SUN Jie;XU Qiang;ZHAO Xiangyuan;GUO Liejin(State Key Laboratory of Multiphase Flow in Power Engineering,Xi’an Jiaotong University,Xi’an 710049,China;School of Oil&Natural Gas Engineering,Southwest Petroleum University,Chengdu 610500,China)

机构地区：[1]西安交通大学动力工程多相流国家重点实验室,西安710049 [2]西南石油大学石油与天然气工程学院,成都610500

出　　处：《工程热物理学报》2024年第11期3398-3405,共8页Journal of Engineering Thermophysics

基　　金：国家重点研发计划(No.2022YFC2806200);国家自然科学基金(No.51888103)。

摘　　要：针对离岸油气管道流动参数时间序列及流型转变的预测,建立基于经验模态分解(EMD)和长短期记忆神经网络(LSTM)组合模型,运用贝叶斯理论对LSTM神经网络相关参数进行优化。相比于单独使用BP神经网络、随机森林算法和LSTM神经网络,本文提出的EMD-LSTM组合预测模型可以更好地追踪立管压差和幅值的演变趋势,大幅度提高预测精度,对流动原始信号及其统计量时间序列均适用。Aiming at the forecasting of time series of flow parameters and flow pattern transformation in offshore oil and gas pipelines,a combined model based on empirical mode decomposition(EMD)and long short-term memory(LSTM)neural networks is established,and Bayesian theory is used to optimize the relevant parameters of LSTM neural network.Compared with BP neural network,random forest algorithm,and LSTM neural network alone,the combined EMD-LSTM prediction model proposed in this paper can better track the evolution trend of riser pressure difference and amplitude,and greatly improve the prediction accuracy.Moreover,it is applicable to both an original flow signal and the time series of its statistical parameters.

关 键 词：集输–立管 经验模态分解 长短期记忆神经网络 瞬态工况 预测 

分 类 号：TE83[石油与天然气工程—油气储运工程]