昆士兰煤层气钻井数据损失时间分析及下一步改进方向

I. Rodger, A. Garnett
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引用次数: 3

摘要

由于需要大量的井,钻井成本是煤层气开发的一个重要因素。为了提高钻井性能(并降低相关成本),应该分析当前的性能,以确定有改进潜力的领域。本研究利用基于最佳复合时间(BCT)的框架,以2014- 2015年为例,对澳大利亚昆士兰州钻井的性能进行了评估。Pason电子钻井记录仪记录了970口井的数据,以及其中370口井的日结报告。用Python编程语言编写的脚本被实现以打破8.5英寸。钻进阶段深入到深度段,并自动生成研究中每个油田的最佳复合时间模型。将单井数据与该基准进行比较,从而将钻井性能与同一油田的其他井进行比较,并将确定的可移动时间分类为无形损失时间(ILT)或非生产时间(NPT)。总共超过4500小时,约占总8.5英寸的49.5%。与现场特定的bct相比,钻井时间被确定为828口井的可拆卸时间。通过分析日常报告中的数字数据和文本数据,确定了ILT和NPT的原因。表现最好和最差的井在关键钻井参数上有明显的区别。底部的ILT与较低的记录RPM相关,而ILT连接与连接前的广泛扩眼和井下清洗相关,这些被认为是可能从数据驱动优化中受益的领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lost Time Analysis of Queensland Coal Seam Gas Drilling Data and Where Next for Improvement?
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.
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