研究快速标记方法,用于指标图的井,基于K-means集群
Xiang Wang1, Zhiwei Shao1, Yancen Shen1
1School of Petroleum and Natural Gas Engineering, Changzhou University, Changzhou, 213164, China.
Heliyon
|October 16, 2023
概括
本研究介绍了用于油井故障诊断的指示图样品标记的高效方法. 原始的矢量集群方法显著加快了数据集准备的速度,并且具有高准确性.
科学领域:
- 石油工程是石油工程中的一个.
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 指标图对于诊断石油抽油井故障至关重要.
- 目前的深度学习故障诊断方法需要大型,准确标记的数据集.
- 手动标记指标图是耗时的,劳动密集的,容易出错的.
研究的目的:
- 开发一种自动化和高效的方法来准备标记指示图数据集用于故障诊断.
- 为了比较不同特征提取和聚类技术的性能,用于指示图分类.
主要方法:
- 提出了三个特征提取方法:原始向量,3D像素张量和卷积神经网络 (CNN).
- 应用K-means集群,根据提取的特征对指标图进行分类.
- 评估方法使用来自100个抽水井的2万个数据样本.
主要成果:
- 最初的矢量方法在0.2小时内达到98%的准确性.
- 3D像素张量法在8.3小时内获得了92%的准确性.
- 在0.7小时内,CNN方法获得了95%的准确性.
结论:
- 原始的基于矢量的集群方法证明了指标图分类的卓越效率和准确性.
- 这种方法比手工标签有了显著的改进,提高了准备效率的几十倍.
- 提供了一个自动化工具,以加快创建用于井故障诊断的数据集.
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