基于实时数据流对比的钻速建模算法

The rate of penetration modeling algorithm based on real-time data stream comparison

摘要:

为提升钻进效率与预测精度，本文设计了一种基于实时数据流对比的钻速建模算法。通过主井与9口历史井的数据流比对，构建了一个面向实时更新与模型复用的预测框架。系统首先利用滑动窗口机制对主井数据动态切片，并以K近邻方式从9口井中提取同深度段数据，构建参考数据集。再结合快速傅里叶变换与频谱相似性指标，实现主井窗口与历史数据的频域对比。当相似度高于设定阈值时，系统复用历史模型，否则即时触发重新建模。建模过程采用随机森林算法，融合主井累计窗口数据与历史近邻数据，并按“80%训练+20%测试”的方式进行训练验证。在最终建模效果中，模型表现出高度的稳定性与良好的泛化能力，平均R2为0.99，残差围绕零值分布。该系统为钻速预测提供了具备实时性、自适应性与可拓展性的建模策略，为智能钻井决策提供了重要支撑。

Abstract:

In order to improve the drilling efficiency and prediction accuracy， this paper designs an ROP modeling algorithm based on real-time data stream comparison. A prediction framework oriented to real-time updating and model reuse is constructed by comparing the data streams of the main well with those of nine historical wells. The system first utilizes the sliding window mechanism to dynamically slice the data of the main well， and extracts the data of the same depth section from the nine wells in a K-Nearest Neighbor manner to construct the reference dataset. Then， it combines the Fast Fourier Transform with the spectral similarity index to realize the frequency domain comparison between the main well window and the historical data. When the similarity is higher than a set threshold， the system reuses the historical model， otherwise it instantly triggers retraining. The modeling process adopts the Random Forest algorithm， fuses the cumulative window data of the main well with the historical near-neighbor data， and carries out training and validation according to the method of “80% training+20% testing”. In the final modeling results， the model shows high stability and good generalization ability， with an average R2 of 0.99 and its residuals distributed around zero. The system provides a real-time， adaptive， and scalable modeling strategy for ROP prediction， which provides important support for intelligent drilling decision-making.