基于GRU神经网络的PDC钻头磨损实时监测模型

Real-Time Monitoring Model of PDC Bit Wear Based on GRU Neural Network

能够实时监测钻头磨损程度对于钻井提速是一个直观的参考目标。但钻井现场难以采集直接反映钻头磨损情况的参数,目前对钻头磨损程度的监测手段较少,主要依靠技术人员的经验判断。如何定量评估PDC钻头磨损程度一直是研究的难点。钻头磨损程度评价主要基于破岩效率和机械比能。通过物理模型计算机械比能,并通过小波分析、聚类算法表征钻头磨损过程,建立了基于门控循环单元(GRU)神经网络的PDC钻头磨损实时监测模型,形成了钻井参数与钻头磨损程度的映射关系,模型精度达95%。采用新疆油田A井数据对模型进行测试,结果表明该模型可以正确预测当前钻头磨损级别。该模型为钻头磨损监测提供了一种解决方案,可以辅助现场工程师判断起下钻时机,以保证更高的钻井效率。

Real-time monitoring of bit wear is crucial for accelerating drilling operations.However,it is challenging to measure on-site parameters that directly reflect levels of bit wear.Currently,there are few means of monitoring bit wear,and in most cases,determination of bit wear is empirically performed by technicians.Quantitatively evaluating the wear of PDC bit has always been a difficult task.The evaluation of bit wear is primarily based on rock breaking efficiency and mechanical specific energy.In this study,a model is proposed for real-time monitoring of PDC bit wear,based on a physical model to calculate mechanical specific energy.Moreover,the wavelet analysis and clustering algorithm are utilized to characterize the bit wear process.Finally,a monitoring model based on Gated Recurrent Unit(GRU)neural network is established,which maps drilling parameters to bit wear levels with 95%accuracy.The model is tested using data from Well A in Xinjiang Oilfield,which demonstrates the capability of the model to accurately estimate current bit wear levels.This model provides a solution for bit wear monitoring,aiding engineers in determining the optimal timing for bit replacement and thereby ensuring higher drilling efficiency.