Prediction Method for ROP Based on Attention Mechanism of Convolutional Neural Network
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NETL
NSTL
万方数据
传统机器学习方法在进行机械钻速(rate of penetration,ROP)预测时,受复杂特征提取和人为认知局限性的影响,难以满足现场预测精度要求.基于此,提出一种特征提取和回归预测相结合的机械钻速预测方法.首先,采用箱型图和独热编码对钻井实测数据进行预处理,清除异常数据并将离散特征连续化.其次,应用卷积神经网络(convolutional neural network,CNN)挖掘数据特征,并在网络中引入通道注意力机制(squeeze-and-excitation network,SENet),实现对CNN特征通道重要性程度的合理分配,建立SE-CNN机械钻速预测模型.最后,将SE-CNN模型与CNN模型进行对比分析,结果表明:SE-CNN模型的拟合优度提高了 2.1％,平均绝对误差和均方根误差分别降低了 1.1％和1.5％.SE-CNN模型具有更高的预测精度,可以用于现场机械钻速预测,为钻井提速提供科学参考.
Traditional machine learning methods for mechanical rate of penetration(ROP)prediction are affected by complex feature extraction and imitations of human understanding,which make the prediction accuracy difficult to meet the on-site demand.Based on this,a new ROP prediction method combining feature extraction and regression prediction was proposed.Firstly,data in drilling engineering were pre-processed by box-plot method and one-hot encoding to eliminate abnormal data and to make discrete characteristic continuous.Secondly,convolutional neural network(CNN)was applied to extracting data features,and channel attention mechanism squeeze-and-excitation network(SENet)was introduced to construct a SE-CNN model,which could adjust the importance of CNN feature channel.Finally,SE-CNN model was compared with CNN model.The results show that the goodness of fit of SE-CNN model is increased by 2.1％,the mean absolute error and the root mean squared error are decreased by 1.1％and 1.5％.SE-CNN model has a better prediction accuracy,and can forecast the ROP of drilling field and provides a scientific reference for increasing the ROP during drilling.
rate of penetrationROP predictionconvolutional neural networkattention mechanism
NETL
NSTL
万方数据
