石油钻采工艺2024，Vol.46Issue(4)：492-508,17.DOI:10.13639/j.odpt.202411056
自然语言方法提取油井修井施工信息提高智能化效率
Extracting oil workover construction information using natural language methods to improve intelligent efficiency
摘要
Abstract
Traditional methods for extracting workover knowledge have the shortcomings of low efficiency provided by manpower and inability to handle large-scale data,resulting in a lack of scientificity in the formulation level of workover measures.For this reason,in the entity extraction stage,an improved attention mechanism based on label weights is designed,which,together with the pre-trained weight model and Bidirectional Long Short-Term Memory(BiLSTM),forms a workover knowledge entity extraction model.In the association rule mining stage,an improved Apriori algorithm that integrates the Bayesian method and the Hash tree is proposed,thus forming a two-stage intelligent analysis and mining method for workover knowledge oriented to the texts of construction plans.Upon being applied in Dagang Oilfield,The results indicate that the recognition accuracy of the workover knowledge entity extraction model can reach 81.83%.The number of frequent itemsets mined by the improved Apriori model is 814,with 515 strongly associated entity combinations,and the computational efficiency of the association rules is increased by 34.38%.The intelligent analysis and mining method for workover knowledge proposed in this article can enhance the efficiency of workover knowledge extraction,providing ideas for data extraction and digital construction in the field of petroleum engineering.关键词
人工智能/大数据/算法/修井/数字经济/室内测试/新质生产力/油气改革Key words
Artificial intelligence/Big data/Algorithm/Workover/Digital economy/Laboratory test/New quality productivity/Oil and gas reform分类
海洋科学引用本文复制引用
杨希军,孔红芳,赵东,易春飚,于国起..自然语言方法提取油井修井施工信息提高智能化效率[J].石油钻采工艺,2024,46(4):492-508,17.基金项目
海上稠油超临界多源多元热流体发生机理及在储层中的作用机制研究(编号:U22B2074). （编号:U22B2074）
