摘要
近年来,随着深度学习的兴起,机器学习在油气领域得到了进一步深入发展。但是,由于油气行业的特殊性和复杂性,目前还没有建成适用于深度学习的训练样本库,也没有针对性的模型建立和选择方法体系。此外,深度学习方法的不可解释性,导致了学习的模型对环境的高度依赖,制约了机器学习在油气行业中的推广应用。从机器学习的发展阶段出发,介绍机器学习在油气行业各领域的应用中所涉及的重大突破及仍然存在的问题。针对油气行业中不同类型数据的处理方法、样本建立以及如何进行模型适应性分析等方面给出了建议,提出可解释机器学习在油气人工智能上的发展潜力以及研究方向。
With the rise of deep learning in recent years,machine learning has been further developed in the oil and gas field.However,due to the particularity and complexity of the oil and gas industry,there is no training sample base suitable for deep learning,nor a targeted model establishment and selection method system.In addition,the uninterpretability of methods such as deep learning leads to the high dependence of learning models on the environment,which restricts the popularization and application of machine learning in the oil and gas industry.Starting from the development stage of machine learning,this paper introduces the major breakthroughs and problems in the application of machine learning in various fields of oil and gas industry.Then,suggestions are given on the processing methods and sample building of different types of data in the oil and gas industry,and how to carry out model adaptability analysis,etc.Finally,the development potential and research direction of machine learning in oil and gas artificial intelligence are proposed.
作者
闵超
代博仁
张馨慧
杜建平
MIN Chao;DAI Boren;ZHANG Xinhui;DU Jianping(School of Science,Southwest Petroleum University,Chengdu,Sichuan 610500,China;Institute for Artificial Intelligence,Southwest Petroleum University,Chengdu,Sichuan 610500,China;Science and Technology Information Center,Zhejiang Oilfield Company,PetroChina,Hangzhou,Zhejiang 311122,China)
出处
《西南石油大学学报（自然科学版）》
CAS
CSCD
北大核心
2020年第6期1-15,共15页
Journal of Southwest Petroleum University(Science & Technology Edition)
基金
国家自然科学基金(11601451)
四川省留学人员科技活动项目择优资助项目(省892)。
关键词
人工智能
机器学习
深度学习
油气行业
综述
artificial intelligence
machine learning
deep learning
oil and gas industry
review
