张剑,肖禹涵,周忠易,等. 基于TDCSO优化CNN-Bi-LSTM网络的井底钻压预测方法[J]. 石油钻探技术,2024,52(5):1-9. DOI: 10.11911/syztjs.2024098
引用本文: 张剑,肖禹涵,周忠易,等. 基于TDCSO优化CNN-Bi-LSTM网络的井底钻压预测方法[J]. 石油钻探技术,2024,52(5):1-9. DOI: 10.11911/syztjs.2024098
ZHANG Jian, XIAO Yuhan, ZHOU Zhongyi, et al. Downhole WOB prediction Method based on CNN-Bi-LSTM network optimized by TDCSO[J]. Petroleum Drilling Techniques, 2024, 52(5):1-9. DOI: 10.11911/syztjs.2024098
Citation: ZHANG Jian, XIAO Yuhan, ZHOU Zhongyi, et al. Downhole WOB prediction Method based on CNN-Bi-LSTM network optimized by TDCSO[J]. Petroleum Drilling Techniques, 2024, 52(5):1-9. DOI: 10.11911/syztjs.2024098

基于TDCSO优化CNN-Bi-LSTM网络的井底钻压预测方法

Downhole WOB prediction Method based on CNN-Bi-LSTM network optimized by TDCSO

  • 摘要: 为了准确预测井底钻压,提高钻井效率、降低钻井成本,建立了融合双向长短期记忆网络(Bi-LSTM)和卷积神经网络(CNN)的混合模型。该模型采用三角函数驱动的粒子群优化(TDCSO)方法进行超参数优化,以实现钻压预测的高精度;采用犹他州FORGE 58-32和FORGE 58-62两口井的公开数据集对建立的模型进行验证,并通过平均绝对误差、均方根误差、决定系数和均方误差等指标进行模型性能评估。结果表明,所提出的TDCSO-CNN-Bi-LSTM模型在MAE、MSE和RMSE三个关键性指标上均取得了优异的成绩,其中 R^2 高达0.98,明显优于现有的LSTM、GRU、CNN-LSTM、CNN-Bi-LSTM等方法。研究表明,所提出的TDCSO-CNN-Bi-LSTM模型在井底钻压预测中具有出色的准确性,能够实现实时监测,并与自动钻进系统集成,实现对钻压的精确控制,不仅提高了钻井效率,还降低了钻井成本,对未来的钻井作业具有重要的实际应用价值。

     

    Abstract: In order to accurately predict the WOB, improve drilling efficiency and reduce drilling cost, a hybrid model combining Bidirectional Long Short-Term Memory Network (Bi-LSTM) and Convolutional Neural Network (CNN) was established. The model uses trigonometric function driven Particle Swarm Optimization (TDCSO) method for hyperparameter optimization to achieve high accuracy of WOB prediction. In the experiment, the public data sets of two Wells in FORGE 58-32 and FORGE 58-62 in Utah were used to verify the established model, and the model performance was evaluated by the mean absolute error, root mean square error, coefficient of determination and mean square error. The results show that the proposed TDCSO-CNN-Bi-LSTM model achieves excellent results in three key indicators of MAE, MSE and RMSE, and the R^2 is as high as 0.98, which is significantly better than the existing LSTM, GRU, CNN-LSTM, CNN-Bi-LSTM and other methods. The study shows that the proposed TDCSO-CNN-Bi-LSTM model has excellent accuracy in DWOB prediction, enables real-time monitoring, and is integrated with an automated drilling system to achieve precise control of WOB. This not only improves the drilling efficiency, but also reduces the drilling cost, which has important practical application value for future drilling operations.

     

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