首页|石油工程大数据算法按应用领域分类提高研究与应用效率

石油工程大数据算法按应用领域分类提高研究与应用效率

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(目的意义)石油工程大数据算法分类不明确,导致查找适用于不同石油工程领域的大数据算法时,准确性和相关性不高.(方法过程)以石油工程勘探、开发、生产和储运四个领域主要现场作业内容为依据,将应用于石油工程领域大数据算法分为勘探领域算法、开发领域算法、生产领域算法和储运领域算法,并厘定 4类算法的概念.从近 10年的核心期刊文献数据库中筛选出涉及油气大数据算法且相关性较强的文献共 53篇,按适用的内容将文献涉及算法归入 4类算法中,形成石油工程领域大数据算法按应用领域划分的分类体系.(结果现象)53篇文献中,勘探领域算法有 7种、开发领域算法有 5种、生产领域算法有 8种、储运领域算法有 7种.分类后,查找算法选择相关率为 100%,算法选择准确率 100%,分别比不分类前平均提高 75个百分点和 52个百分点.(结论建议)按照应用领域将石油工程大数据算法分为 4大类,解决了在选择适用算法时准确率低和相关性不强的难题,同时为其他领域文献分类提供了借鉴方法.
Petroleum engineering big data algorithms categorized by application fields to improve research and application efficiency
The classification of big data algorithms in petroleum engineering is not well-defined,leading to suboptimal accuracy and low relevance when searching for big data algorithm applications across different fields of petroleum engineering.Based on the main on-site operating activities in the four fields of petroleum engineering covering exploration,development,production,and storage/transportation,the big data algorithms engaged in petroleum engineering are categorized into four groups:exploration algorithms,development algorithms,production algorithms,and storage/transportation algorithms.The concepts of these four types of algorithms are clarified.Totally 53 papers,highly relevant with oil and gas big data algorithms,are picked from core journal databases over the past decade,from core journal databases over the pase decade.The algorithms involved in these papers are introduced into these four algorithm categories according to the applicable contents,creating the classification of big data algorithms in petroleum engineering by fields.In terms of algorithm in 53 papers,there are 7 subdivisions of exploration algorithms,5 of development algorithms,8 of production algorithms,and 7 of storage/transportation algorithms.After classification,the accuracy rate for selecting algorithms reached 100%,and the efficiency of algorithm selection also reached 100%,which is an improvement of 75 percentage points and 52 percentage points,respectively,compared to the pre-classification phase.Categorizing big data algorithms in petroleum engineering into four main groups based on application fields resolves the issues of low accuracy and weak relevance in selecting appropriate algorithms for research and applications big data algorithms.This method also provides a useful reference for classifying literature in other fields.

Oil and gas resourcesExploration and developmentEngineering technologyPetroleum engineeringBig dataDigital transformationMachine learningAlgorithms

金龙、李慧娟、苏丹丹、王宝山、董文才、孙金声、郑力会

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中国石油大学(北京)石油工程学院,北京昌平

中国教育科学研究院教育统计分析研究所,北京海淀

中国石油华北油田公司油气工艺研究院,河北任丘

中国石油集团渤海钻探工程有限公司井下作业分公司,河北任丘

中国石油华北油田公司公共事务中心,河北任丘

中国石化胜利油田分公司现河采油厂,山东东营

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油气资源 勘探开发 工程技术 石油工程 大数据 数字转型 机器学习 算法

2024

石油钻采工艺
华北油田分公司 华北石油管理局

石油钻采工艺

CSTPCD北大核心
影响因子:0.975
ISSN:1000-7393
年,卷(期):2024.46(4)