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机器学习在油气田开发中的应用、挑战及发展趋势

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随着石油勘探开发领域生产数据的不断激增,整合和分析难度日益加大,人工分析法难以满足油气田现场的开发需求。机器学习可精准高效地实现数据的处理,是数据分析和优化领域中的一项关键技术,利用机器学习处理油气田开发中的疑难问题已成为业内的主攻方向。但由于受标准化建设、样本库建立、现场融合度等方面的限制,机器学习在石油勘探开发领域中的应用尚存在不少挑战和不足,未来应致力于加强机器学习标准化建设、提高深度学习算法的数据处理能力、深度机器学习领域的学科交叉,多管齐下,共同推进机器学习在油气田行业内的长足发展。
Application,challenge and development trend of machine learning in oil and gas field development
With the continuous surge of production data in the field of oil exploration and development,the difficulty of integration and analysis is increasing,and manual analysis methods are difficult to meet the development needs of oil and gas fields on site.Machine learning can accurately and efficiently process data,and is a key technology in the field of data analysis and optimization.Utilizing machine learning to solve difficult problems in oil and gas field development has become the main focus of the industry.How-ever,due to limitations in standardization construction,sample library establishment,and on-site integra-tion,there are still many challenges and shortcomings in the application of machine learning in the field of oil exploration and development.In the future,efforts should be made to strengthen the standardization construction of machine learning,improve the data processing ability of deep learning algorithms,and pro-mote interdisciplinary research in the field of deep machine learning.Through multiple approaches,we can jointly promote the significant development of machine learning in the oil and gas field industry.

machine learningoil exploration and developmenton site integration degreechallengedevelopment

闫春明、陈孔全、李洪燕、芦升彦、王砚锋、庞一桢

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长江大学 地球科学学院,湖北 武汉 430100

长庆油田分公司第六采油厂,陕西 榆林 719000

中国海油中联煤层气有限公司,山西 太原 030000

机器学习 石油勘探开发 现场融合度 挑战 发展

非常规油气省部共建协同创新中心开放基金项目湖北省高等学校优秀中青年科技创新团队计划项目

UOG2022-36T201905

2024

应用化工
陕西省石油化工研究设计院 陕西省化工学会

应用化工

CSTPCD北大核心
影响因子:0.411
ISSN:1671-3206
年,卷(期):2024.53(8)