[1]尚福华,张月霞,曹茂俊.基于知识图谱的测井储层推荐算法研究[J].计算机技术与发展,2023,33(04):132-139.[doi:10. 3969 / j. issn. 1673-629X. 2023. 04. 020]
　SHANG Fu-hua,ZHANG Yue-xia,CAO Mao-jun.Research on Logging Reservoir Recommendation Algorithm Based onKnowledge Graph[J].,2023,33(04):132-139.[doi:10. 3969 / j. issn. 1673-629X. 2023. 04. 020]
点击复制

基于知识图谱的测井储层推荐算法研究()

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

卷:
33
期数:
2023年04期
页码:
132-139
栏目:
人工智能
出版日期:
2023-04-10

文章信息/Info

Title:
Research on Logging Reservoir Recommendation Algorithm Based onKnowledge Graph
文章编号:
1673-629X(2023)04-0132-08
作者:
尚福华; 张月霞; 曹茂俊
东北石油大学 计算机与信息技术学院,黑龙江 大庆 163318
Author(s):
SHANG Fu-hua; ZHANG Yue-xia; CAO Mao-jun
School of Computer and Information Technology,Northeast Petroleum University,Daqing 163318,China
关键词:
知识图谱; 知识图谱补全; 测井储层信息; 注意力机制; 推荐算法
Keywords:
knowledge graph; completion of knowledge graph; logging reservoir parameters; attention mechanism; recommendation algorithm
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 04. 020
摘要:
针对实际应用中测井解释人员由于经验不足以及测井解释模型所选取的参数不当、差错等,所造成的测井处理解释结果的评价精度不准等问题,提出了一种基于知识图谱的测井储层推荐算法。 首先,构建测井领域知识图谱对测井解释操作人员和其测井储层特性信息进行统一表示;其次,通过加入注意力机制和 TransR 算法对测井领域知识图谱进行补全,再通过连接机制把推荐算法与知识图谱结合起来;最后,根据潜在特征预测并推荐测井领域中储层参数信息。 在测井领域数据集上的实验结果表明,该推荐算法在 Top-K 推荐中 K = 15 时,其准确率、召回率和归一化折损累计增益三个指标比同类算法分别提高了 6. 21% 、8. 05% 、4. 82% ,并能够高精度预测和推荐测井领域储层参数信息以及更准确地判断储层油气状况,从而揭示出基于知识图谱构建测井储层推荐算法的研究方案是可行的。
Abstract:
In order to solve the problems of inaccurate evaluation accuracy of logging interpretation results caused by inexperience oflogging interpreters and improper parameters and errors of logging interpretation model in practical application, a reservoirrecommendation algorithm based on knowledge graph is proposed. Firstly,
the logging domain knowledge map is constructed to representthe logging interpretation operators and their logging reservoir characteristics uniformly. Secondly, attention mechanism and TransRalgorithm are added?
to complete the log domain knowledge map,the recommendation algorithm is combined with the knowledge graph byconnecting mechanism. Finally, the reservoir parameter information in logging field is predicted and recommended according to thepotential characteristics. Experimental results on logging data sets show that when K = 15 in Top-K recommendation,the accuracy,recallrate and normalized loss cumulative gain of the proposed algorithm are 6. 21% , 8. 05% and 4. 82% higher than those of similaralgorithms respectively. And?
it can predict and recommend reservoir parameter information in logging field with high precision and judge reservoir oil and gas condition more accurately,thus revealing that the research scheme of constructing logging reservoir recommendationalgorithm based on knowledge graph is feasible.

相似文献/References:

[1]孙艳,田丽梅. 基于多维尺度分析的舆情研究主题词知识图谱[J].计算机技术与发展,2016,26(04):187.
　SUN Yan,TIAN Li-mei. Mapping Knowledge Domain on Subject Headings of Public Sentiment Research Based on Multi-dimensional Scaling[J].,2016,26(04):187.
[2]刘申凯,周霁婷,朱永华,等.融合知识图谱和 ESA 方法的网络新词识别[J].计算机技术与发展,2019,29(03):12.[doi:10.3969/ j. issn.1673-629X.2019.03.003]
　LIU Shen-kai,ZHOU Ji-ting,ZHU Yong-hua,et al.Network New Word Recognition Based on Fusion of Knowledge Graph and ESA[J].,2019,29(04):12.[doi:10.3969/ j. issn.1673-629X.2019.03.003]
[3]戈其平,钟艳如.基于数学教学的知识图谱构建[J].计算机技术与发展,2019,29(03):187.[doi:10.3969/ j. issn.1673-629X.2019.03.039]
　GE Qi-ping,ZHONG Yan-ru.Construction of Knowledge Atlas Based on Mathematics Teaching[J].,2019,29(04):187.[doi:10.3969/ j. issn.1673-629X.2019.03.039]
[4]魏 瑾,李伟华,潘 炜.基于知识图谱的智能决策支持技术及应用研究[J].计算机技术与发展,2020,30(01):1.[doi:10. 3969 / j. issn. 1673-629X. 2020. 01. 001]
　WEI Jin,LI Wei-hua,PAN Wei.Research on Intelligent Decision Support Technology and Application Based on Knowledge Graph[J].,2020,30(04):1.[doi:10. 3969 / j. issn. 1673-629X. 2020. 01. 001]
[5]项 威,王 邦.中文事件抽取研究综述[J].计算机技术与发展,2020,30(02):1.[doi:10. 3969 / j. issn. 1673-629X. 2020. 02. 001]
　XIANG Wei,WANG Bang.Survey of Chinese Event Extraction Research[J].,2020,30(04):1.[doi:10. 3969 / j. issn. 1673-629X. 2020. 02. 001]
[6]刘家祝,郭 强,吴碧伟,等.基于子图相交的社交账号与知识图谱实体对齐[J].计算机技术与发展,2020,30(05):10.[doi:10. 3969 / j. issn. 1673-629X. 2020. 05. 003]
　LIU Jia-zhu,GUO Qiang,WU Bi-wei,et al.Subgraph Intersection Based Alignment between Social Media Account and Knowledge Graph Entity[J].,2020,30(04):10.[doi:10. 3969 / j. issn. 1673-629X. 2020. 05. 003]
[7]陆菁宇,张绍阳,黄文旎.学科发展状态的知识图谱构建[J].计算机技术与发展,2020,30(06):145.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 028]
　LU Jing-yu,ZHANG Shao-yang,HUANG Wen-ni.Analysis of Development Status of Discipline Based on Knowledge Graph[J].,2020,30(04):145.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 028]
[8]黄东晋,秦 汉,郭 昊.基于 BERT-CNN 的电影原声智能问答系统[J].计算机技术与发展,2020,30(11):158.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 029]
　HUANG Dong-jin,QIN Han,GUO Hao.Movie Soundtrack Intelligent Question and Answer System Based on BERT-CNN[J].,2020,30(04):158.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 029]
[9]任佳妮,杨 阳.全球医疗机器人技术领域创新态势分析[J].计算机技术与发展,2021,31(04):158.[doi:10. 3969 / j. issn. 1673-629X. 2021. 04. 027]
　REN Jia-ni,YANG Yang.Analysis of Innovation Situation in Field of Global MedicalRobot Technology[J].,2021,31(04):158.[doi:10. 3969 / j. issn. 1673-629X. 2021. 04. 027]
[10]卢 琪,谢艺菲,谢 钧,等.知识图谱在智能问答中的应用研究[J].计算机技术与发展,2021,31(07):13.[doi:10. 3969 / j. issn. 1673-629X. 2021. 07. 003]
　LU Qi,XIE Yi-fei,XIE Jun,et al.Research on Application of Knowledge Graphs in Intelligent Question Answering[J].,2021,31(04):13.[doi:10. 3969 / j. issn. 1673-629X. 2021. 07. 003]

更新日期/Last Update: 2023-04-10