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A Structured Approach to Benchmarking Bit Runs and Identifying Good Performance for Optimization of Future Applications

机译:基准测试位运行的结构化方法,并识别优化未来应用的良好性能

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Performance analysis of large numbers of bit runs is often anecdotal and uses historical cost data. To this end, there are numerous problems with this approach. There is no uniform approach to identifying good performance. At best, the analysis provides an imprecise picture of overall performance. Large datasets need to be condensed into runs of interest. Difficulties arise when comparing multiple runs through long intervals with variable thicknesses of hard stringers. Since BHA, rig, and other costs change over time, it is problematic using historical cost per foot (CPF) data for the current target well. Finally, how does one determine if long slow runs or short fast ones are better since both could have the same CPF? In this paper, the authors discuss a structured benchmarking method that can be applied regardless of the application or area studied. The basic process is simple and can be tailored to the requirements of different applications. The goal is to deliver a statistical benchmarking process that helps filter large sets of data and facilitates a consistent approach to bit performance analysis that is independent of historical cost data. A process flow chart is developed to guide engineers step-by-step through the benchmarking method. Good offsets are identified and included in the benchmarking population. Eligible bit runs are then ranked by a new key performance indicator (KPI): ROP*Distance Drilled. No historical cost data is included in the analysis. A detailed engineering study is then carried out on the identified best runs to develop recommendations for future applications. As the last step of the process, a financial analysis is carried out using cost data for the current well. The paper will describe the use of this process to analyze bit performance in the operator’s gas drilling operation and show how it allowed the identification of ‘true’ unbiased top performance. The benchmarking process standardizes performance analysis and ensures sound engineering principles are applied resulting in a better understanding of past performance and better recommendations for future applications.
机译:大量位运行的性能分析通常是轶事,并使用历史成本数据。为此,这种方法存在许多问题。没有统一的方法来识别良好的性能。最多,分析提供了整体性能的不精确图片。大型数据集需要融入兴趣的运行中。在使用具有可变厚度的硬度串的长时间间隔时,难以进行困难时出现困难。由于BHA,钻机和其他成本随着时间的推移而变化,因此使用目前目标的历史成本(CPF)数据是有问题的。最后,自从两者都可以具有相同的CPF,如何确定长时间慢速运行或短快速速度是否更好?在本文中,作者讨论了一种结构化的基准测试方法,无论研究所研究的应用程序或区域如何。基本过程简单,可以根据不同应用程序定制。目标是提供一个统计基准测试过程,有助于过滤大集的数据并促进与历史成本数据无关的比特性能分析的一致方法。开发过程流程图以通过基准方法逐步引导工程师。良好的抵消被识别并包含在基准批量中。然后按新的关键绩效指标(KPI)排名符合条件的位运行:ROP *距离钻取。分析中没有包含历史成本数据。然后对已识别的最佳运行进行详细的工程研究,以为未来的应用程序制定建议。作为该过程的最后一步,使用当前井的成本数据进行财务分析。本文将描述使用此过程来分析操作员气体钻井操作中的位性能,并展示如何允许识别“真实”的无偏见性能。基准工艺标准化性能分析,并确保应用了声音工程原则,从而更好地了解过去的性能和更好的未来应用建议。

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