西南石油大学学报（自然科学版）2025，Vol.47Issue(6)：1-14,14.DOI:10.11885/j.issn.1674-5086.2023.10.29.31
大数据在井筒工程的应用和发展
Application and Development of Big Data in Well Engineering
摘要
Abstract
Industrial 4.0 technological revolution promotes the oil and gas industry to enter a new stage of smart oilfields,which is characterized by digitalization and intelligence.China has made great progress in digital construction and application integration in the oil field,carrying out a number of big data analysis on massive exploration and development data collected,such as drilling,logging,well testing,analysis and testing,oil and gas production,and accelerating automated construction and intelligent decision-making.However,there are some challenges,such as inconsistent standards of well engineering database,difficulties in in-depth data sharing,severe data isolation and so on.In order to accelerate the construction and application of geology-engineering integration and better leverage the big data of well engineering for the construction of smart oilfield,the following work has been carried out:the relationship and difference between big data of well engineering and traditional big data are analyzed;the current situation of oil and gas big data platforms at home and abroad is counted;the characteristics and levels of well data are introduced;the big data algorithm of common well engineering problems is summarized;the scheme of algorithm optimization according to business requirements is proposed.Finally,the development suggestions are put forward for the current problems existing in the application of current big data technology in well engineering.关键词
井筒工程/大数据/人工智能/油气云平台/地质-工程一体化Key words
well engineering/big data/artificial intelligence/oil and gas cloud platform/geology-engineering integration分类
能源科技引用本文复制引用
ZHANG Zhi,WANG Xianghui,DING Jian,ZHAO Jie,WU Linfang,HOU Zhenyong..大数据在井筒工程的应用和发展[J].西南石油大学学报（自然科学版）,2025,47(6):1-14,14.基金项目
国家科技重大专项(2024ZD1406603,2025ZD1402205,2025ZD1401106) （2024ZD1406603,2025ZD1402205,2025ZD1401106）
国家自然科学基金区域创新发展联合基金重点支持项目(U22A20164) （U22A20164）
