XGSleeve:通过使用XGBoost分类器检测井完工中的套件事件
Sahand Somi1, Sheikh Jubair1, David Cooper2
1Advanced Technology, Alberta Machine Intelligence Institute, Edmonton, AB, Canada.
Frontiers in artificial intelligence
|October 2, 2023
概括
本研究介绍了XGSleeve,这是一种用于检测石油开采中滑动袖事件的机器学习方法. 它通过减少重复的袖子尝试的需要来提高运营效率和安全性.
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
- 人工智能的人工智能
背景情况:
- 滑动袖子对于液体流量控制在页岩油提取的液压破裂中至关重要.
- 目前用于验证袖子状态的方法,如深孔摄像头,成本高昂且效率低下.
- 重复的袖子打开尝试导致过程低效率和可靠性问题.
研究的目的:
- 开发一种成本效益高,数据驱动的方法来检测滑动袖事件.
- 提高石油和天然气行业井完成操作的可靠性和效率.
- 引入XGSleeve方法论,用于实时识别袖子事件.
主要方法:
- 利用下洞数据分析进行袖子事件检测,取代昂贵的摄像头.
- 开发了XGSleeve方法,将基于隐藏马尔科夫模型 (HMM) 的聚类与XGBoost模型结合起来.
- 应用时间序列分类和信号处理技术,以实现 robust 事件识别.
主要成果:
- XGS袖子模型在检测滑动袖子事件时达到86%的精度.
- 显著减少了多次袖子打开关闭尝试的必要性.
- 证明了对高成本的洞内摄像头系统的可行替代方案.
结论:
- 该XGSleeve型号在滑动袖事件检测方面取得了重大进展,提高了运营效率和安全性.
- 这种数据驱动的方法优化了石油和天然气运营,促进了弹性和负责任的资源管理.
- 强调人工智能和机器学习在能源领域更广泛的整合和可持续实践方面的潜力.
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