为充分运用钻井监测平台多个监测因子的长时序信息, 实现海上石油钻井卡钻事故的准确预测, 提出一种多因子长时序信息联合建模的深度卷积卡钻预测方法(CNN-MFT), 利用自注意力机制结合卷积网络对多个监测因子的时序信息进行联合建模, 同时考虑当前时刻各因子的具体值的信息以及各因子的历史时序信息, 实现准确的卡钻预测.使用海上钻井平台实际监测数据开展验证对比, 与目前常用的基于随机森林(RF)、SVM等8种卡钻预测方法相比, 所提的CNN-MFT方法在50%和70%等不同训练样本比例的条件下, 其卡钻事故预测准确率最高, 且稳定性强, 可为海上石油事故预测应用提供关键算法支撑.
To make full use of the long time series information of multiple monitoring factors obtained by a drilling monitoring platform and implement accurate prediction of sticking accidents in offshore oil drilling, this study proposes a deep convolution sticking prediction method based on joint modeling of multi-factor long time series information (CNN-MFT). It uses the self-attention mechanism and a CNN to jointly model the time series information of multiple monitoring factors. Meanwhile, it considers the specific value of each factor at the current moment and the historical time series information of each factor to achieve accurate sticking prediction. Verification and comparison are conducted with actual monitoring data on the offshore drilling platform. Compared with the eight commonly used sticking prediction methods such as those based on random forest (RF) and support vector machine (SVM), the proposed CNN-MFT achieves the best accuracy of sticking accident prediction under different training sample proportions, 50% and 70% for example. Meanwhile, it is also more stable. This method provides key algorithm support for applications of offshore oil accident prediction.