基于人工智能的抽油机井结蜡预警方法

针对依靠现场经验确定的清蜡周期不准确而导致蜡卡躺井的问题，开展了基于人工智能的抽油机井结蜡预警方法研究。利用皮尔逊相关系数分析方法，分析了17项油井自动采集参数与结蜡程度的关联性，确定了7项主控参数，创建了结蜡预警规则模型；将7项主控参数的合并指标进行归一化处理得到结蜡综合特征指标（WPSC），并利用结蜡预警规则模型产生的样本数据建立了结蜡井WPSC样本集，选用长短时记忆神经网络（LSTM）对样本集进行训练，得到了WPSC机器学习模型，用其可以定量预测抽油机井的结蜡程度。该方法在胜利油田桩23区块的现场应用结果表明，油井清蜡周期得到延长，且有效避免了蜡卡躺井。研究结果表明，基于人工智能的抽油机井结蜡预警方法实现了油井结蜡程度的定量化预测与预警，对精准确定清蜡时机具有较好的指导作用。 

An Early Warning Method Based on Artificial Intelligence for Wax Deposition in Rod Pumping Wells

The timing of wax clearance is usually determined in the field by observations, which is inaccurate and may cause wells to fail due to wax locking. To solve this problem, a wax deposition early warning method based on artificial intelligence for rod pumping wells has been developed. The Pearson correlation coefficient analysis method is used to conduct correlation analysis between 17 points of data automatically acquired by oil well condition and degree of wax deposition, and seven main control parameters are determined. A wax deposition early warning rule protocol was established on this basis. The wax deposition synthetic characteristics (WPSC) index was obtained by normalizing the merged indexes of seven main control parameters, and the WPSC sample set of wells with paraffin problems was established by using the sample data generated by the wax deposition early warning rule model. Long-term and short-term memory (LSTM) neural networks were selected to train the sample set, and the machine learning model of WPSC is obtained, which could quantitatively predict the degree of wax deposition in the rod pumping wells. The field trial of this method in Block Zhuang 23 of the Shengli Oilfield showed that by using this method, the wax clearance timing as prolonged and the wax locking in failed wells was effectively avoided. Research results showed that the wax deposition early warning method based on artificial intelligence for rod pumping wells achieves quantitative prediction and provides effective early warning on the wax deposition degree in oil wells, and could be used as an effective guide in precise the selection of wax clearance timing.