使用机器学习分析由于井口不干净而导致的管道粘接
Aseel A1, Aslam Abdullah M1, Rithul Roy1
1Petroleum Engineering Research Group, School of Chemical Engineering, Vellore Institute of Technology, Vellore, 632014, India.
Heliyon
|December 11, 2023
概括
这项研究开发了一种预测模型,用于石油钻井中的管道粘接. 一个集成分类器实现了90%的准确性,提供实时检测以防止昂贵的停机时间.
科学领域:
- 石油工程是石油工程中的一个.
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 管道粘接是石油钻探中的重大运营挑战,导致大量停机时间和成本增加.
- 井口不干净是导致管道堵塞事故的主要原因.
- 有效的预测模型对于缓解这些问题至关重要.
研究的目的:
- 开发一个准确和有效的预测模型,用于由井口不干净引起的管道粘接.
- 为了评估各种分类算法的性能,用于管道粘合预测.
- 确定最适合实时检测和预防的算法.
主要方法:
- 预处理和清理两个印度水库的钻探数据.
- 多种机器学习分类算法的应用和比较.
- 基于准确性,概括能力和对杂数据的稳定性进行性能评估.
主要成果:
- 与单个分类算法相比,整体分类算法表现出优异的性能.
- 整体分类器的平均准确度约为90%,可以实现.
- 集合分类器显示了高的概括能力和对噪音数据的免疫力.
结论:
- 集成分类算法对于预测由于井口不干净而导致的管道粘接非常有效.
- 开发的模型为实时检测提供了强有力的支持,大大减少了昂贵的停机时间.
- 机器学习为石油钻探业务中持续存在的挑战提供了强大的解决方案.
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