{"title":"Lost Time Analysis of Queensland Coal Seam Gas Drilling Data and Where Next for Improvement?","authors":"I. Rodger, A. Garnett","doi":"10.2118/192034-MS","DOIUrl":null,"url":null,"abstract":"\n Due to the high number of wells required, drilling costs are a significant factor for coal seam gas developments. In order to improve drilling performance (and reduce associated costs) current performance should be analysed to identify areas with potential for improvement. This study makes use of a framework based on the best composite time (BCT) to assess the performance of wells drilled in Queensland, Australia in an example period in 2014-15.\n Data recorded by Pason electronic drilling recorders at 970 wells was made available, along with end-of-day reports for 370 of these wells. Scripts written in the Python programming language were implemented to break the 8½ in. drilling stage down into depth sections and automatically generate a best composite time model for each field in the study. Individual well data was compared to this benchmark allowing the drilling performance to be compared to other wells in the same field, and identified removable time was classified as either invisible lost time (ILT) or non-productive time (NPT). In total over 4500 hours, or approximately 49.5% of the total 8½ in. drilling time, was identified as removable time across 828 wells when compared to field specific BCTs.\n Causes of ILT and NPT were identified by analysing both numerical data and textual data in daily reports. There is a clear separation in key drilling parametes between the best and worst performing wells. ILT while on bottom correlated with lower recorded RPM, while ILT connecting was associated with extensive reaming and down-hole-cleaning prior to connections, and these are identified as areas which may benefit from data driven optimisation.","PeriodicalId":11182,"journal":{"name":"Day 3 Thu, October 25, 2018","volume":"73 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 3 Thu, October 25, 2018","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/192034-MS","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Due to the high number of wells required, drilling costs are a significant factor for coal seam gas developments. In order to improve drilling performance (and reduce associated costs) current performance should be analysed to identify areas with potential for improvement. This study makes use of a framework based on the best composite time (BCT) to assess the performance of wells drilled in Queensland, Australia in an example period in 2014-15.
Data recorded by Pason electronic drilling recorders at 970 wells was made available, along with end-of-day reports for 370 of these wells. Scripts written in the Python programming language were implemented to break the 8½ in. drilling stage down into depth sections and automatically generate a best composite time model for each field in the study. Individual well data was compared to this benchmark allowing the drilling performance to be compared to other wells in the same field, and identified removable time was classified as either invisible lost time (ILT) or non-productive time (NPT). In total over 4500 hours, or approximately 49.5% of the total 8½ in. drilling time, was identified as removable time across 828 wells when compared to field specific BCTs.
Causes of ILT and NPT were identified by analysing both numerical data and textual data in daily reports. There is a clear separation in key drilling parametes between the best and worst performing wells. ILT while on bottom correlated with lower recorded RPM, while ILT connecting was associated with extensive reaming and down-hole-cleaning prior to connections, and these are identified as areas which may benefit from data driven optimisation.