计算机科学
知识图
井口
推论
领域知识
井控
实体链接
图形
人工智能
钻探
知识库
数据挖掘
机器学习
理论计算机科学
石油工程
工程类
机械工程
作者
Sheng Wei,Yanchun Liang,Xiaoran Li,Xiaohui Weng,Jiasheng Fu,Xiaosong Han
出处
期刊:Entropy
[MDPI AG]
日期:2023-07-22
卷期号:25 (7): 1097-1097
摘要
Managed pressure drilling (MPD) is the most effective means to ensure drilling safety, and MPD is able to avoid further deterioration of complex working conditions through precise control of the wellhead back pressure. The key to the success of MPD is the well control strategy, which currently relies heavily on manual experience, hindering the automation and intelligence process of well control. In response to this issue, an MPD knowledge graph is constructed in this paper that extracts knowledge from published papers and drilling reports to guide well control. In order to improve the performance of entity extraction in the knowledge graph, a few-shot Chinese entity recognition model CEntLM-KL is extended from the EntLM model, in which the KL entropy is built to improve the accuracy of entity recognition. Through experiments on benchmark datasets, it has been shown that the proposed model has a significant improvement compared to the state-of-the-art methods. On the few-shot drilling datasets, the F-1 score of entity recognition reaches 33%. Finally, the knowledge graph is stored in Neo4J and applied for knowledge inference.
科研通智能强力驱动
Strongly Powered by AbleSci AI