{"title":"将时间序列数据转换成图像:一种检测煤层气井螺杆泵异常行为的创新方法","authors":"Fahd Saghir, M. G. Perdomo, P. Behrenbruch","doi":"10.2118/195905-ms","DOIUrl":null,"url":null,"abstract":"\n Progressive Cavity Pumps (PCPs) are the predominant form of artificial lift method deployed by Australian operators in Coal Seam Gas (CSG) wells. With over five thousand CSG wells [1] operating in Queensland's Bowen and Surat Basins, managing and maintaining PCP supported production becomes a significant challenge for operators. Especially when these pumps face regular failures due to the production of coal fines.\n It is possible to gauge the holistic production performance of PCPs with the aid of real-time data, as this allows for pro-active and informed management of artificially lifted CSG wells. Based on data obtained from two (2) CSG operators, this paper will discuss in detail how features extracted from time series data can be converted to images, which can then aid in autonomously detecting abnormal PCP behavior.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"50 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Converting Time Series Data into Images: An Innovative Approach to Detect Abnormal Behavior of Progressive Cavity Pumps Deployed in Coal Seam Gas Wells\",\"authors\":\"Fahd Saghir, M. G. Perdomo, P. Behrenbruch\",\"doi\":\"10.2118/195905-ms\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Progressive Cavity Pumps (PCPs) are the predominant form of artificial lift method deployed by Australian operators in Coal Seam Gas (CSG) wells. With over five thousand CSG wells [1] operating in Queensland's Bowen and Surat Basins, managing and maintaining PCP supported production becomes a significant challenge for operators. Especially when these pumps face regular failures due to the production of coal fines.\\n It is possible to gauge the holistic production performance of PCPs with the aid of real-time data, as this allows for pro-active and informed management of artificially lifted CSG wells. Based on data obtained from two (2) CSG operators, this paper will discuss in detail how features extracted from time series data can be converted to images, which can then aid in autonomously detecting abnormal PCP behavior.\",\"PeriodicalId\":10909,\"journal\":{\"name\":\"Day 2 Tue, October 01, 2019\",\"volume\":\"50 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Day 2 Tue, October 01, 2019\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2118/195905-ms\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 2 Tue, October 01, 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/195905-ms","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Converting Time Series Data into Images: An Innovative Approach to Detect Abnormal Behavior of Progressive Cavity Pumps Deployed in Coal Seam Gas Wells
Progressive Cavity Pumps (PCPs) are the predominant form of artificial lift method deployed by Australian operators in Coal Seam Gas (CSG) wells. With over five thousand CSG wells [1] operating in Queensland's Bowen and Surat Basins, managing and maintaining PCP supported production becomes a significant challenge for operators. Especially when these pumps face regular failures due to the production of coal fines.
It is possible to gauge the holistic production performance of PCPs with the aid of real-time data, as this allows for pro-active and informed management of artificially lifted CSG wells. Based on data obtained from two (2) CSG operators, this paper will discuss in detail how features extracted from time series data can be converted to images, which can then aid in autonomously detecting abnormal PCP behavior.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
