在海上压裂井的沙子堵塞实时预警方法
Yantao Xu1,2, Bumin Guo3,4, Wen Zhang3,4
1China Oilfield Services Limited, Binhai New Area, Tianjin, 300459, China. Xuyt4@cosl.com.cn.
Scientific reports
|February 19, 2025
概括
这项研究介绍了一种智能系统,用于在液压压裂过程中实时检测沙子堵塞. 这种新的方法通过提供及时警告和准确的识别来提高运营安全和效率.
科学领域:
- 石油工程是石油工程中的一个.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 沙塞是导致水力压裂失败的主要原因之一.
- 目前的检测方法缓慢,不准确,缺乏实时功能.
研究的目的:
- 开发一种智能方法,用于实时识别和警告沙堵塞.
- 为了提高在水力压裂中检测沙塞的准确性和效率.
主要方法:
- 利用了海上水力压裂数据.
- 基于员工注意力机制的长期短期记忆网络 (Att-LSTM) 进行实时压力预测.
- 开发了一个基于注意力机制的卷积长期短期记忆网络 (Att-CNN-LSTM) 用于沙塞识别.
- 集成Att-LSTM和Att-CNN-LSTM与转移学习进行持续学习.
主要成果:
- 在40秒内,Att-LSTM在压力预测中实现了>92%的准确性.
- 亚特-CNN-LSTM识别了带有<1分钟错误率的沙塞.
- 集成系统显著提高了检测沙塞的准确性和效率.
结论:
- 拟议的智能系统提供了一种新且有效的解决方案,用于实时发出沙塞堵塞警告.
- 这种方法提高了水力压裂作业的安全性和效率.
- 该研究为破裂的智能决策提供了关键的技术支持.
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