基于长短时记忆神经网络的抽油机井故障智能预警

准确预测抽油机井故障对油田生产具有重要意义。针对新疆油田某区块抽油机井故障情况，统计了500口油井的生产数据，明确了结垢、结蜡、杆管腐蚀、杆管疲劳、杆管偏磨五种引发抽油机井故障的主要因素；基于长短时记忆神经网络（Long Short-Term Memory Networks, LSTM），构建了油井故障智能预警模型；筛选出影响油井故障的14种特征参数进行小波降噪处理，借助自适应矩估计算法对模型进行训练与测试。研究结果表明，模型预测准确率为96.81%，能够为油田提供较为准确的抽油机井故障预警信息。

Research on Intelligent Early Warning of Pumping Machine Well Fault Based on Long Short-Term Memory Neural Network

Accurately predicting the fault of rod-pumped wells is of great significance for oilfield production. Aiming at the fault situation of rod-pumped wells in a block of Xinjiang Oilfield, the production data of 500 wells were collected, and the 5 main factors causing the fault of pumping Wells were identified, such as scale, wax formation, rod corrosion, rod fatigue and rod partial wear; Based on Long Short-Term Memory Networks (LSTM), the intelligent early-warning model of oil well failure is constructed; By selecting 14 characteristic parameters that affect oil well faults for wavelet denoising, the model is trained and tested with the help of adaptive moment estimation algorithm. The findings suggest that the prediction accuracy of the model is 96.81%, which can provide more accurate early warning for rod-pumped well faults.