摘要
在井下振动信号向高频采集发展趋势下,井下振动采集模块需要存储和传输的数据量逐渐增大。为了解决井下数据存储和上传压力大的问题,并对井下钻具的运行状态进行预警,提出了将压缩感知理论和支持向量机(Support Vector Machine,SVM)模型融入于井下振动信号的存储、传输和状态预警中。研究了一种原子数自适应的稀疏字典建立方法,用少量稀疏特征表达完整信号;建立了观测矩阵将原始信号投影到低维空间上,实现信号的压缩方法;应用改进的布谷鸟算法(Improved Cuckoo Search,ICS)对SVM模型进行参数寻优,训练好的ICS-SVM模型实现了钻具状态预警。应用结果表明,压缩感知技术可以将井下振动数据压缩至12%,数据重构误差为0.1772,ICS-SVM模型对钻具状态预警成功率达到98%。研究结果达到了缓解井下振动数据存储和上传压力的目的,可帮助工作人员更好地进行实时钻井操作和状态预警。
As the acquisition of downhole vibration signals develops towards high-frequency acquisition,the data volume required to be stored and transmitted by the downhole vibration acquisition module is gradually increasing.In order to solve the difficult problems of downhole data storage and uploading and provide early warning for the operating status of downhole drilling tool,the compressed sensing theory and support vector machine(SVM)model were integrated into the downhole vibration signal storage,transmission and downhole drilling tool status warning.An atomic number adaptive sparse dictionary building method was studied to use a small number of sparse features to express a complete signal.An observation matrix was built to project the original signal onto a low dimensional space to achieve signal compression.The improved cuckoo search(ICS)was applied for parameter optimization of the SVM model,and the trained ICS-SVM model achieved drilling tool status warning.The application results show that the compressed sensing technology can compress downhole vibration data to 12%,with a data reconstruction error of 0.1772,and the success rate of ICS-SVM model for drilling tool status warning reaches 98%.The research results have achieved the goal of alleviating the pressure of storing and uploading downhole vibration data,which helps working personnel better carry out real-time drilling operations and status warnings.
作者
李飞
王一帆
吕方兴
Li Fei;Wang Yifan;LüFangxing(School of Electronic Engineering,Xi'an Shiyou University/Directional Drilling Branch,CNOOC Key Laboratory of Well Logging and Directional Drilling/Xi'an Key Laboratory of Intelligentization Equipment Development for Oil,Gas and Renewable Energy)
出处
《石油机械》
北大核心
2024年第9期1-9,共9页
China Petroleum Machinery
基金
国家自然科学基金企业创新发展联合基金重点项目“复合式旋转导向钻井工具的理论与方法研究”(U20B2029)
国家重点研发技术项目“海洋石油大直径指向式旋转导向系统研制”(2023YFC2810902)
陕西省自然科学基金青年项目“非常规油气开发中游标增敏光纤应变传感机理及关键技术的研究与应用”(2023-JC-QN-0405)
陕西秦创原“科学家+工程师”团队项目(2022kxj-125)
西安石油大学研究生创新与实践能力培养计划(YCS23114124)。
关键词
井下振动信号
高频采集
压缩感知
布谷鸟算法
支持向量机
钻具状态预警
downhole vibration signal
high-frequency acquisition
compressed sensing
cuckoo search
support vector machine
drilling tool status warning
