石油钻采工艺2024，Vol.46Issue(4)：395-412,18.DOI:10.13639/j.odpt.202409013
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
Petroleum engineering big data algorithms categorized by application fields to improve research and application efficiency
摘要
Abstract
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.关键词
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Key words
Oil and gas resources/Exploration and development/Engineering technology/Petroleum engineering/Big data/Digital transformation/Machine learning/Algorithms分类
能源科技引用本文复制引用
金龙,李慧娟,苏丹丹,王宝山,董文才,孙金声,郑力会..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[J].石油钻采工艺,2024,46(4):395-412,18.基金项目
国家科技重大专项"多气合采钻完井技术和储层保护"(编号:2016ZX05066002) （编号:2016ZX05066002）
中国石油大学(北京)研究生教育质量与创新工程项目"研究生科研能力定量化跟踪培养及实践"(编号:yjs2017034). （北京）
