在管理式压力钻井领域中,中国少数名的命名实体识别和知识图构建
Siqing Wei1, Yanchun Liang1,2, Xiaoran Li1
1Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
本研究引入了一个MPD知识图表,以增强井控制自动化. 一个新的CEntLM-KL模型提高了实体识别准确度,以实现更安全,更智能的钻井操作.
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
- 知识管理知识管理
背景情况:
- 管理式压力钻井 (MPD) 通过精确的井头反压控制来提高钻井安全.
- 目前的MPD井井控制策略严重依赖于人工经验,限制了自动化和智能化.
- 复杂的钻井条件需要先进的,数据驱动的井控制解决方案.
研究的目的:
- 开发一个MPD知识图,用于指导自动化井控制.
- 为了提高知识图表构建的实体提取性能.
- 提高MPD井口控制策略的准确性和效率.
主要方法:
- 通过从已发表的论文和钻探报告中提取信息,构建一个MPD知识图.
- 将EntLM模型扩展到CEntLM-KL,用于对少数中国实体的认可,并纳入KL.
- 使用Neo4J来存储知识图,并启用知识推理.
主要成果:
- 与最先进的方法相比,拟议的CEntLM-KL模型在实体识别方面取得了重大改进.
- 在一些射击钻井数据集上获得33%的F-1评分,用于实体识别.
- 成功应用MPD知识图用于知识推理.
结论:
- 开发的MPD知识图和CEntLM-KL模型为自动化和智能井控制提供了可行的方法.
- 增强实体识别准确性对于MPD中有效的知识图表构建至关重要.
- 知识图和先进的人工智能模型的整合有望提高钻井的安全性和效率.
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