Tu Bingrui, Bai Kai, Zhan Ce, Zhang Wanxing
Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Yangtze University, Wuhan, China.
Xi'an Key Laboratory of Tight Oil (Shale Oil) Development (Xi'an Shiyou University), Xi'an, 710065, Shaanxi, China.
Sci Rep. 2024 Jan 25;14(1):2133. doi: 10.1038/s41598-024-52261-7.
Accurate ROP (rate of penetration) prediction contributes to better production task planning, ensuring efficient production line operation, and reducing production costs. ROP prediction is influenced by multiple factors, making accurate prediction challenging. Current research primarily relies on historical data for training and modeling, lacking methods for real-time ROP prediction. This paper introduces a GRU-Informer model for real-time ROP prediction. The model employs GRU (Gated Recurrent Unit) neural networks at the lower level to capture short-term correlations in drilling parameters and uses the Informer model at the top to address long-term dependencies among drilling parameters. Thus, the GRU-Informer can capture both short-term and long-term time dependencies, providing better ROP predictions. This paper constructs a dataset using historical data from a southwestern Chinese oil field for experimentation. RMSE (Root Mean Square Error), MAE (mean absolute error) and [Formula: see text] (Coefficient of Determination) are employed as evaluation metrics for the model. Experimental results demonstrate that the GRU-Informer outperforms traditional recurrent neural networks like LSTM (Long Short-Term Memory), GRU neural networks and Informer in real-time ROP prediction, indicating its practical value.
准确的机械钻速（ROP）预测有助于更好地进行生产任务规划，确保生产线高效运行，并降低生产成本。机械钻速预测受多种因素影响，使得准确预测具有挑战性。当前的研究主要依赖历史数据进行训练和建模，缺乏实时机械钻速预测方法。本文介绍了一种用于实时机械钻速预测的GRU-Informer模型。该模型在较低层级采用门控循环单元（GRU）神经网络来捕获钻井参数中的短期相关性，并在顶层使用Informer模型来处理钻井参数之间的长期依赖性。因此，GRU-Informer可以捕获短期和长期时间依赖性，提供更好的机械钻速预测。本文利用中国西南部某油田的历史数据构建数据集进行实验。均方根误差（RMSE）、平均绝对误差（MAE）和决定系数（R²）被用作该模型的评估指标。实验结果表明，在实时机械钻速预测方面，GRU-Informer优于传统循环神经网络，如长短期记忆网络（LSTM）、GRU神经网络和Informer，表明了其实际应用价值。
