【摘要】 为提升钻井过程已发溢流风险的识别准确率，融合特征工程与机器学习方法，建立一种多类别溢流风险智能识别模型。首先，基于现场实测溢流数据，采用小波变换实现噪声抑制;其次，结合光滑样条函数提取关键参数的动态变化趋势，据此分析溢流特征参数的异常波动行为，建立低、中、高3类风险等级的判定准则，根据钻井数据的变化特征标记溢流风险;然后，引入麻雀搜索算法(SSA)优化极限学习机(ELM)，构建基于改进型ELM(IELM)的多分类溢流风险智能识别模型;最后，所构建的风险数据集进行模型训练、调优与测试，验证模型的识别性能。结果表明:IELM模型在分类精度与判别稳定性方面均优于原始ELM及反向传播(BP)神经网络模型，能够更准确和高效地识别不同等级的溢流风险。更多还原

【Abstract】 In order to improve the identification accuracy of kick risk during drilling,a multi-category kick risk intelligent identification model was proposed by integrating feature engineering and machine learning techniques. Firstly,a wavelet transform was employed to achieve noise suppression based on fieldmeasured kick data. Secondly,the dynamic variation trends of key parameters were extracted using smooth spline functions,and the abnormal fluctuation behaviors of kick-related characteristic parameters were analyzed. Based on this, a three-level risk classification criterion(low, medium, and high) was established,and kick risks were labeled according to the variation features of drilling data. Then,the sparrow search algorithm(SSA) was introduced to optimize the extreme learning machine(ELM),and a multi-classification kick risk intelligent identification model based on IELM was constructed. Finally,the performance of the model was validated through training,tuning,and testing on the constructed risk dataset. The results show that the IELM model outperforms the original ELM and back-propagation(BP)neural network model in terms of classification accuracy and discrimination stability,and is capable of identifying different levels of kick risks more accurately and efficiently.更多还原