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

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.