首页|基于压缩感知的井下钻具状态预警方法研究

基于压缩感知的井下钻具状态预警方法研究

扫码查看
原文链接
  • 万方数据
  • 维普
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0.177 2,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.
Method of Downhole Drilling Tool Status Warning Based on Compressed Sensing
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 increas-ing.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 di-mensional space to achieve signal compression.The improved cuckoo search(ICS)was applied for parameter opti-mization 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 re-construction error of 0.177 2,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.

downhole vibration signalhigh-frequency acquisitioncompressed sensingcuckoo searchsupport vector machinedrilling tool status warning

李飞、王一帆、吕方兴

展开 >

西安石油大学电子工程学院

中海油集团测井与定向钻井重点实验室定向钻井分室

西安市油气及新能源开发装备智能化重点实验室

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

国家自然科学基金企业创新发展联合基金重点项目国家重点研发技术项目陕西省自然科学基金青年项目陕西秦创原"科学家+工程师"团队项目西安石油大学研究生创新与实践能力培养计划

U20B20292023YFC28109022023-JC-QN-04052022kxj-125YCS23114124

2024

10.16082/j.cnki.issn.1001-4578.2024.09.001

石油机械
中国石油天然气集团公司装备制造分公司 中国石油学会石油工程专业委员会 江汉机械研究所 江汉石油管理局

石油机械

CSTPCD北大核心
影响因子：0.737
ISSN：1001-4578
年,卷(期)：2024.52(9)