用次优数据集验证钻探状态分类器

Luis R. Pereira
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引用次数: 1

摘要

基于钻机数据的分析技术的广泛应用,为海上钻井行业各个层面的性能、质量和安全性的提高提供了大量机会。随着自动化和高风险决策开始更多地依赖于这些类型的分类器,需要考虑的一个主题是在开发过程中使用的验证方法,以确保准确性和精度,需要最好的可用方法来帮助数据科学家评估它们的可靠性、特征和局限性,并向可能不熟悉这些技术的关键利益相关者解释。在从信号数据确定钻井状态的特殊情况下,可能会出现地面真实记录的分辨率低于预期的情况,或者在标记上存在一定程度的不确定性,诸如内部可靠性(IRR)或内部一致性(IRA)之类的技术可以帮助证明多个来源提供的观测决策之间的一致性,并用作显示以下方面的一致程度的方法,例如:同时使用钻台数据和现有的IADC代码的钻井状态生成器分类器。在进行更严格的模型验证之前,这种方法可以用来帮助对特定分类器的进一步开发做出决策。本文将展示这些技术应用于使用信号数据与日志记录自动生成某些IADC代码的示例,以及IRR/IRA如何帮助通知结果的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Validating Drilling States Classifiers with Suboptimal Datasets
The wide-scale deployment of analytics to support the well construction processes based on rig data has opened a host of opportunities to improve performance, quality, and safety at all levels in the offshore drilling industry. As automation and high-stakes decision making starts to rely more on these types of classifiers, a topic of consideration is the validation methods employed during their development to ensure accuracy and precision, requiring the best available methods to help data scientists evaluate their soundness, features and limitations, and explain to key stakeholders who may not be familiar with such techniques. In the particular case of drilling states determination from signal data, there may be cases where the ground truth records are either at lower resolution than desired, or where some degree of uncertainty on the labeling exist, techniques such as inter-rater reliability (IRR) or inter-rater agreement (IRA) can help to demonstrate consistency among observational decision provided by multiple sources and be used as a way to show the level of agreement between, for example, a proposed drilling state generator classifier using drillfloor data and existing IADC codes from available logs at the same time. This approach can be used to help decisions on further development of the particular classifier before committing to stricter model validation. This paper will show examples of these techniques applied to automatic generation of certain IADC codes using signal data vs log records, and how IRR/IRA can help inform the quality of the results.
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