数据挖掘在石油勘探数据库中的应用前景
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摘要
通过世界现状分析,指出数据挖掘(Data Mining,以下简称DM)在石油勘探数据库中的应用尚处于起步阶段。在前人的工作基础上,简述了数据库DM技术的结构、功能、算法及关键技术。以一个测井解释实例具体介绍DM的操作过程、技术方法及应用效果,验证该技术的可行性、实用性。实例中,采用多元回归分析,实现数据降维;分别采用人工神经网络和支持向量机两个挖掘算法,进行知识发现。基于国内大批石油勘探数据库(包括数据银行、数据仓库等)的陆续建成,认为DM技术的研发已提到议事日程,必将成为石油地质研究和勘探决策的有力手段。
This paper indicates through worldwide analysis that the application of data mining (DM) in petroleum exploration databases is in the initial stage. It briefly describes the structures, functions, algorithms and key techniques of data mining based on the work of pioneer contributors. A case study of well-logging interpretation introduces the processes, methods and application results of DM in concrete terms, proving that the DM used is feasible and practical. In the case study, multiple regression analysis is adopted as a dimension-reduction algorithm, while artificial neural network and support vector machine are employed as mining algorithms for knowledge discovery. Since a great number of petroleum exploration databases (including databanks and datastores) have been or will be built in China, it is time to further develop the techniques of DM that will become powerful tools for petroleum geology research and exploration decision.
This paper indicates through worldwide analysis that the application of data mining (DM) in petroleum exploration databases is in the initial stage. It briefly describes the structures, functions, algorithms and key techniques of data mining based on the work of pioneer contributors. A case study of well-logging interpretation introduces the processes, methods and application results of DM in concrete terms, proving that the DM used is feasible and practical. In the case study, multiple regression analysis is adopted as a dimension-reduction algorithm, while artificial neural network and support vector machine are employed as mining algorithms for knowledge discovery. Since a great number of petroleum exploration databases (including databanks and datastores) have been or will be built in China, it is time to further develop the techniques of DM that will become powerful tools for petroleum geology research and exploration decision.
引文
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[23]王国鹏,郭齐军,赵庆,等.确定特低渗油气层有效压裂的下限值——以泌阳凹陷安棚油田为例[J].西南石油学院学报,2006,28(4):40～43.
[24]申辉林,高松洋.基于BP神经网络进行裂缝识别研究[J].断块油气田,2007,14(2):60～62.
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[26]Guangren Shi,Xingxi Zhou,Guangya Zhang,et al.The use of artificial neural network analysis and multiple regression for trap quality evaluation:A case study of the Northern Kuqa Depression of Tarim Basin in western China.Marine and Petroleum Geology,2004,21(3):411～420.
[27]石广仁.支持向量机在多地质因素分析中的应用[J].石油学报,2008,29(2):195～198.
[2]Hand D,Mannila H,Smyth P.Principles of Data Mining.Cambridge,MA,USA:MIT Press,2001.
[3]Larose D T.Discovering Knowledge in Data.New York:John Wiley&Sons,Inc.,2005.
[4]Larose D T.Data Mining Methods and Models.New York:John Wiley&Sons,Inc.,2005.
[5]Wong P M.A novel technique for modeling fracture intensity:A case study from the Pinedale anticline in Wyoming.AAPG Bulletin,2003,87(11):1717～1727.
[6]Aminzadeh F.Applications of AI and soft computing for challenging problems in the oil industry.Journal of Petroleum Science and Engineering,2005,47(1～2):5～14.
[7]Mohaghegh S D.A new methodology for the identification of best practices in the oil and gas industry,using intelligent systems.Journal of Petroleum Science and Engineering,2005,49(3～4):239～260.
[8]雷景生.基于POSC平台的油气勘探数据仓库及数据挖掘[J].计算机工程,2003,29(20):175～176,190.
[9]段新民,陈清华,李琴.石油勘探开发数据信息与数据挖掘[J].情报杂志,2004,23(7):111～112.
[10]汪忠德,王新海,瞿建华,等.数据挖掘技术在石油勘探与开发中的研究及应用[J].石油工业计算机应用,2007,15(1):17～20.
[11]赵睿.人工智能和地理信息系统技术在石油勘探开发中集成应用的一种设想[J].中国石油勘探,2007,12(3):64～68.
[12]肖基毅,胡蓉,王以群.油网格数据挖掘新技术研究[J].西南石油大学学报,2008,30(3):152～154.
[13]周霞.油田勘探数据库建设及应用技术[J].中国石油勘探,2003,8(3):87～91.
[14]常冠华,金成志,王荣华,等.大庆油田勘探信息化体系结构及目标[J].中国石油勘探,2004,9(4):66～71.
[15]冯宗祥.油气勘探生产信息平台建设的目标及方法[J].中国石油勘探,2005,10(3):53～56.
[16]罗海炳,王刚.辽河油田勘探信息化建设及应用情况[J].中国石油勘探,2005,10(4):75～77.
[17]宋学功,吴亚珉.华北油田建成三维地震资料数据库[J].石油工业计算机应用,2006,14(1):51～51.
[18]许忠.数据库提速吐哈油田勘探开发[J].石油工业计算机应用,2007,15(1):52～53.
[19]王晓莲,顾娟,王颖,等.大庆油田测井数据库系统设计[J].油气田地面工程,2007,26(2):46～47.
[20]唐传章,王瑞贞,宋同心,等.华北油田三维地震资料品质分析数据库的开发及应用效果[J].中国石油勘探,2008,13(2):83～89.
[21]Han J W,Kamber M.Data mining:concepts and techniques.2nd edition.San Fransisco:Morgan Kaufmann,2006.
[22]明海会,金振奎,李清作,等.泌阳凹陷安棚油田深层系沉积相及对油气分布的控制作用[J].地球科学与环境学报,2005,27(2):48～51.
[23]王国鹏,郭齐军,赵庆,等.确定特低渗油气层有效压裂的下限值——以泌阳凹陷安棚油田为例[J].西南石油学院学报,2006,28(4):40～43.
[24]申辉林,高松洋.基于BP神经网络进行裂缝识别研究[J].断块油气田,2007,14(2):60～62.
[25]石广仁.地学中的计算机应用新技术[M].北京:石油工业出版社,1999.
[26]Guangren Shi,Xingxi Zhou,Guangya Zhang,et al.The use of artificial neural network analysis and multiple regression for trap quality evaluation:A case study of the Northern Kuqa Depression of Tarim Basin in western China.Marine and Petroleum Geology,2004,21(3):411～420.
[27]石广仁.支持向量机在多地质因素分析中的应用[J].石油学报,2008,29(2):195～198.
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