基于模型融合的钻进参数识别岩石类型研究

Data‑driven model for the identification of the rock type by drilling data

岩石类型的识别对于钻进工程的安全和经济效益具有重要意义。钻进过程中的岩石类型实时识别大多是通过随钻测井,但由于随钻测井成本昂贵,在地质勘探领域鲜见应用。本文挖掘深部探测Sinoprobe-05项目金川科钻的钻进多参数(钻速、钻压、转速、钻头扭矩、泵压力、泵量)数据,通过一种融合模型算法来识别岩石类型。首先采用Savitzky-Golay平滑滤波器降低钻进参数数据的噪声,然后对数据进行了归一化。最后运用融合模型预测和识别岩石类别。融合模型的初级学习器为支持向量机、人工神经网络和随机森林,通过次级学习器贝叶斯模型平均算法对每个模型的权重进行计算。结果表明,多模型融合算法准确率为0.9686,比每个单独的算法准确率高。

The identification of rock types is of great significance to the safety and economic benefits of drilling engineering.Real‑time rock type during drilling.The identification is mostly through logging while drilling,but it is rarely used in the field of geological exploration due to the high cost of logging while drilling.In this paper,the drilling parameters(ROP,WOB,rotational speed,bit torque,pump pressure,pump volume)of Jinchuan Scientific Drill are used to identify rock types through a fusion model.First,the noise of the drilling parameter data is reduced by the Savitzky-Golay smoothing filter,and then the data is normalized.Finally,the fusion model is used to predict rock types.The primary learners of the fusion model are Support Vector Machines,Artificial Neural Networks and Random Forests,and the weights of each model are calculated by the secondary learner Bayesian model averaging algorithm.The results show that the accuracy of the multi‑model fusion algorithm is 0.9686,which is higher than that of each individual algorithm.