基于机器学习和贝叶斯优化的大位移井钻井提速方法  

Drilling Speed Enhancement Method for Extended Reach Wells Based on Machine Learning and Bayesian Optimization

作　　者：张伟国 蒋昆 宋宇[2] 邓成辉 黄懿强 马溢 ZHANG Weiguo;JIANG Kun;SONG Yu;DENG Chenghui;HUANG Yiqiang;MA Yi(Engineering Technology Department,CNOOC Limited.,Beijing,100053,China;College of Artificial Intelligence,China University of Petroleum(Beijing),Beijing,102249,China;Shenzhen Branch of CNOOC Limited,Shenzhen,Guangdong,518000,China)

机构地区：[1]中国海洋石油有限公司工程技术部,北京100053 [2]中国石油大学(北京)人工智能学院,北京102249 [3]中海石油(中国)有限公司深圳分公司,广东深圳518000

出　　处：《石油钻探技术》2025年第2期38-45,共8页Petroleum Drilling Techniques

基　　金：国家重点研发计划项目“深水生产系统管外应急抢修处置技术及装备研制”(编号:2022YFC28061004);国家自然科学基金项目“深水水下井口系统耦合动力学及承载力演化机制研究”(编号:52204017)联合资助。

摘　　要：海上大位移井井眼轨迹复杂,水平位移较大,导致井下摩阻高,严重影响了钻井效率。为此,根据钻井数据和录井数据等,提出了一种基于机器学习钻速预测与钻井参数优化的大位移井钻井提速方法。首先,对现场原始数据进行滤波、归一化等预处理,进行相关性分析,发现钻压、转盘转速等钻井参数及井斜角、水平位移等井眼轨迹参数与钻速有显著的相关性;然后,构建了基于BP神经网络、随机森林和支持向量机的钻速预测模型,评价结果表明,BP神经网络模型表现最优,可以较为准确地预测海上大位移井的机械钻速;最后,采用贝叶斯优化算法,以提高钻速为目标对钻压、转盘转速和排量等参数进行优化。优化结果表明,钻井参数优化后,机械钻速平均提升了18.86%。研究结果揭示了钻井参数和井眼轨迹参数对大位移井钻速的影响规律,为大位移井钻井提速提供了理论依据。The complex wellbore trajectories and significant horizontal displacements of offshore extended reach wells lead to increased downhole friction,severely affecting drilling efficiency.By leveraging drilling and logging data,this study proposes a novel method for rate of penetration(ROP)enhancement in extended reach wells with ROP prediction and drilling parameter optimization based on machine learning.Firstly,the original field data were preprocessed by filtering and normalization,followed by correlation analysis,revealing that ROP has strong correlation with drilling parameters such as weight on bit(WOB)and rotary speed,as well as wellbore trajectory parameters such as inclination angle and horizontal displacement.Based on these findings,ROP prediction models were developed using BP neural networks,random forests,and support vector machines.The results show that the BP neural network model outperforms the others,providing relatively accurate ROP predictions for offshore extended reach wells.Finally,the Bayesian optimization algorithm was employed to optimize parameters such as WOB,rotary speed,and displacement for ROP enhancement.The optimization results show that the ROP increases by 18.86%on average after the optimization of drilling parameters.The research results reveal the influence of drilling parameters and wellbore trajectory parameters on the ROP of extended reach wells and provide a theoretical basis for increasing the ROP of extended reach wells.

关 键 词：钻井参数 机械钻速 机器学习 贝叶斯优化 大位移井 

分 类 号：TE21[石油与天然气工程—油气井工程] TE243.1