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		题名	作者	年代	出处	被引量
	1	基于支持向量机回归的机械钻速智能预测显示文摘塔里木盆地深层油气资源丰富,但随着井深的增加地层可钻性降低、研磨性升高,导致机械钻速低,钻井成本高等难题,亟需钻井优化技术。机械钻速预测是优化钻井的关键技术之一,准确的机械钻速预测可以为钻井参数优化、钻井工具优选等提供重要依据。利用钻井现场可实时获得的录井数据,基于决策树回归算法、随机森林回归算法、支持向量机回归算法和深度神经网络分别建立了机械钻速智能预测模型。从均方根误差、R平方、最大误差和相对误差四个方面进行对比分析,从而优选最优的机械钻速智能预测模型。结果表明支持向量机回归模型的预测精度、稳定性均优于其他模型。	宋先知裴志君王潘涛张宫凌燕叶山林	2022	新疆石油天然气2022，18，1：	5
	2	Application of machine learning and artificial intelligence in oil and gas industry显示文摘Oil and gas industries are facing several challenges and issues in data processing and handling.Large amount of data bank is generated with various techniques and processes.The proper technical analysis of this database is to be carried out to improve performance of oil and gas industries.This paper provides a comprehensive state-of-art review in the field of machine learning and artificial intelligence to solve oil and gas industry problems.It also narrates the various types of machine learning and artificial intelligence techniques which can be used for data processing and interpretation in different sectors of upstream oil and gas industries.The achievements and developments promise the benefits of machine learning and artificial intelligence techniques towards large data storage capabilities and high efficiency of numerical calculations.In this paper a summary of various researchers work on machine learning and artificial intelligence applications and limitations is showcased for upstream and sectors of oil and gas industry.The existence of this extensive intelligent system could really eliminate the risk factor and cost of maintenance.The development and progress using this emerging technologies have become smart and makes the judgement procedure easy and straightforward.The study is useful to access intelligence of different machine learning methods to declare its application for distinct task in oil and gas sector.	Anirbid Sircar Kriti Yadav Kamakshi Rayavarapu Namrata Bist Hemangi Oza	2021	Petroleum Research2021，6，4：	3

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