石油科学通报2025，Vol.10Issue(5)：926-940,15.DOI:10.3969/j.issn.2096-1693.2025.03.010
基于1dCNN-BiGRU和注意力机制的钻井工况智能识别方法
Intelligent recognition method for drilling conditions based on 1dCNN-BiGRU and attention mechanism
摘要
Abstract
This study addresses the challenges of poor real-time performance and low accuracy in drilling condition identification by introducing an innovative intelligent recognition method.The proposed approach integrates a one-dimensional convolutional neural network(1dCNN)for local feature extraction,a bidirectional gated recurrent unit(BiGRU)to capture sequential dependencies,and a multi-head attention mechanism to emphasize critical information.This fusion enables efficient discrimination among 13 drilling conditions,including rotary drilling,slide drilling,whipstocking,and reverse whipstocking.In the model design phase,comprehensive ablation studies were conducted to evaluate the contributions of each module—1dCNN,BiGRU,self-attention,and multi-head attention—as well as their serial and parallel configurations.The performance was further optimized using the Optuna framework for automatic hyperparameter tuning.Experimental results demonstrated that the model achieved an accuracy of 96.22%on time-domain data from a single well.Additionally,in both intra-and inter-block transfer tests,the overall accuracy ranged from 94%to 97%,with each drilling condition exceeding an 80%recognition rate.Real-time testing on field data also showed a high degree of consistency with actual operational conditions.Overall,the proposed method provides a robust technical framework for real-time monitoring and optimization of drilling operations.关键词
油气钻井/钻井工况/人工智能/神经网络/注意力机制Key words
drilling/drilling conditions/artificial intelligence/neural networks/self-attention分类
能源科技引用本文复制引用
王正,宋先知,李洪松,于佳伟,王一帆,张重愿..基于1dCNN-BiGRU和注意力机制的钻井工况智能识别方法[J].石油科学通报,2025,10(5):926-940,15.基金项目
国家自然科学基金委员会国家自然科学基金-国家杰出青年科学基金(52125401)资助 （52125401）
