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Lost Time Analysis of Queensland Coal Seam Gas Drilling Data and Where Next for Improvement?

机译:昆士兰煤层气钻井数据的损失时间分析及接下来改善?

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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-ofday reports for 370 of these wells. Scripts written in the Python programming language were implemented to break the 8? 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 8? 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.
机译:由于所需的井数量大,钻井成本是煤层气开发的重要因素。为了提高钻井性能(并降低相关成本),应分析目前的性能以识别具有改进潜力的区域。本研究利用了基于最佳复合时间(BCT)的框架,以评估澳大利亚澳大利亚昆士兰州钻井井的性能。 Pason电子钻床记录在970个井中记录的数据,以及最终报告,其中370孔。在Python编程语言中编写的脚本被实施以打破8?在。钻探阶段深入部分,并自动为研究中的每个领域生成最佳的复合时间模型。将个体井数据与该基准进行比较,允许钻探性能与相同领域中的其他孔进行比较,并且所识别的可移动时间被归类为无形的丢失时间(ILL)或非生产时间(NPT)。总共超过4500小时,占总数的约49.5%?与现场特定BCT相比,钻井时间在828孔中被识别为可拆卸时间。通过在日常报告中分析数值数据和文本数据来识别ILT和NPT的原因。在最佳和最差的井之间的关键钻头参数中有明显的分离。在底部与较低记录的RPM相关的同时,在连接之前,截口连接与大量的铰孔和下孔清洁相关联,并且这些被识别为可能从数据驱动优化中受益的区域。

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