[1]王 昆,李文倚,刘月田,等.基于改进图神经网络的油藏开发知识检索方法[J].计算机技术与发展,2025,(10):158-165.[doi:10.20165/j.cnki.ISSN1673-629X.2025.0128]
　WANG Kun,LI Wen-yi,LIU Yue-tian,et al.Reservoir Development Knowledge Retrieval Method Based on Improved Graph Neural Network[J].,2025,(10):158-165.[doi:10.20165/j.cnki.ISSN1673-629X.2025.0128]
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基于改进图神经网络的油藏开发知识检索方法

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年10期
页码:
158-165
栏目:
人工智能
出版日期:
2025-10-10

文章信息/Info

Title:
Reservoir Development Knowledge Retrieval Method Based on Improved Graph Neural Network
文章编号:
1673-629X(2025)10-0158-08
作者:
王 昆1; 2; 李文倚1; 刘月田2; 岳 翔1; 武若楠1
1. 中海油研究总院有限责任公司勘探开发数据资源中心,北京 100028; 2. 中国石油大学(北京) 人工智能学院,北京 102249
Author(s):
WANG Kun1; 2; LI Wen-yi1; LIU Yue-tian2; YUE Xiang1; WU Ruo-nan1
1. Exploration and Development Data Resource Centre,CNOOC Research Institute Co. ,Ltd. ,Beijing 100028,China; 2. School of Artificial Intelligence,China University of Petroleum,Beijing 102249,China
关键词:
油藏开发; 知识图谱; 自适应机制; 图神经网络; 广度优先搜索
Keywords:
reservoir development; knowledge graph; adaptive mechanism; graph neural network; breadth first search
分类号:
TP391.3
DOI:
10.20165/j.cnki.ISSN1673-629X.2025.0128
摘要:
基于传统检索方法在理解用户检索意图和处理复杂查询时的局限性,该文提出并改进了一种基于自适应图神经网络的油藏开发知识图谱检索方法,即IagRdkgr模型,以提升检索的准确性和效率。该文采用自适应机制动态调整图神经网络的结构和参数,并结合广度优先搜索算法优化图谱推理,引入基于关系权重的消息传递机制、动态邻接节点信息聚合及自适应节点表示方法,以增强模型对知识图谱复杂结构和上下文信息的理解能力。实验结果表明,IagRdkgr模型在油藏开发知识检索任务中的准确率达到98%,能够更高效地帮助用户获取所需知识,并提供直观的展示方式,从而提升决策的科学性和准确性。
Abstract:
Due to the limitations of traditional retrieval methods in understanding users' retrieval intentions and processing complex queries,we propose and improve a reservoir development knowledge graph retrieval method based on adaptive graph neural network,namely IagRdkgr model,to improve the accuracy and efficiency of retrieval. In this study,an adaptive mechanism is used to dynamically adjust the structure and parameters of graph neural networks,and a breadth- first search algorithm is combined to optimize graph inference. A message passing mechanism based on relationship weights,dynamic adjacency node information aggregation and an adaptive node representation method are introduced to enhance the model's understanding of complex structure and context information of knowledge graphs. The experimental results show that the accuracy of IagRdkgr model in reservoir development knowledge retrieval tasks reaches 98%,which can help users obtain the required knowledge more efficiently and provide intuitive display methods to improve the scientific and accurate decision making.

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更新日期/Last Update: 2025-10-10