基于模型融合的钻进参数识别岩石类型研究
投稿时间:2022-10-20  修订日期:2022-12-28  点此下载全文
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作者单位邮编
王亚飞 中铁第四勘察设计院集团有限公司 430063
张占荣 中铁第四勘察设计院集团有限公司 430063
刘华吉 中铁第四勘察设计院集团有限公司 430063
姚震桐 中国地质大学(武汉)工程学院 430074
基金项目:湖北省重点研发计划项目(2021BAA050):城市地下空间精细化探测与感知关键技术及装备;国家重点研发计划(2019YFC0605101):城市地下空间开发地下全要素信息精准探测技术与装备;
中文摘要: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,并未在地质勘探领域广泛应用。本文使用金川科钻的钻进参数(钻速、钻压、转速、钻头扭矩、泵压力、泵量)通过一种融合模型识别岩石类型。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0.9643,比每个单独的算法准确率高。
中文关键词:岩石类型  钻进参数  支持向量机  随机森林  模型融合
 
Data-driven model for the identification of the rock type by drilling data
Abstract: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 not widely 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 0.9643, which is higher than that of each individual algorithm.
keywords:rock type  drilling parameters  support vector machine  random forest  model fusion
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