摘要
旋转导向钻井系统是油气开发领域的高端装备,其核心部分——动态指向式旋转导向钻井工具的可靠运行是钻井系统正常工作的重要前提.本文基于模型的方法研究了导向钻井工具装备的微小故障检测,有望为系统的运行维护与健康管理提供重要保障.首先,基于机理建模方法得到了导向钻井工具装备的数学模型.然后,利用移动加权平均方法提高了残差对微小故障的敏感性.基于非中心χ^(2)分布的性质,实现了对微小故障在统计意义下的可检测性分析,得到了保证误报率与漏报率在允许范围内的窗口长度和权值.最后,在旋转导向钻井工具原理样机上进行实验,实验结果验证了所提方法的有效性.
Rotary steering drilling system is a high-end piece of equipment in oil and gas development. The reliable operation of its core equipment, the dynamic point-the-bit rotary steerable drilling tool(DPRSDT), is an important prerequisite for the normal operation of the drilling system. In this paper, the problem of incipient fault detection is investigated for the DPRSDT equipment based on the model method, which is expected to provide an important guarantee for the operation, maintenance, and health management of the system. First,the mathematical model of the DPRSDT is derived using the mechanism modeling method. Then, the moving weighted average approach is employed to improve the sensitivity of residuals to incipient faults. Based on the characteristics of the noncentral χ^(2) distribution, the detectability of incipient faults is analyzed in a statistical sense, and the window length and weight are derived, which ensures that the false alarm rate and missed detection rate can be reduced below the tolerance level. Finally, an experiment is performed in the rotary steerable drilling tool prototype to verify the effectiveness of the proposed method.
作者
盛立
牛艺春
刘诗洋
王伟亮
高明
周东华
Li SHENG;Yichun NIU;Shiyang LIU;Weiliang WANG;Ming GAO;Donghua ZHOU(College of Control Science and Engineering,China University of Petroleum,Qingdao 266580,China;College of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao 266590,China)
出处
《中国科学：信息科学》
CSCD
北大核心
2022年第11期2106-2120,共15页
Scientia Sinica(Informationis)
基金
国家自然科学基金(批准号:62173343,62073339,62033008)
山东省自然科学基金(批准号:ZR2020YQ49)
山东省泰山学者项目研究基金资助项目。
关键词
旋转导向钻井工具装备
微小故障检测
故障可检测性分析
误报率
漏报率
rotary steerable drilling tool equipment
incipient fault detection
fault detectability analysis
false alarm rate
missed detection rate
