油气层综合解释系统的开发与应用
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摘要
以人工神经网络技术和模糊隶属度统计方法为理论基础,在SUN工作站环境下开发了油气层综合解释系统。该系统用于识别岩心、岩屑、井壁取心、测井、气测和钻井等6大类46个参数。对于其中的定性参数,通过模糊统计确定参数不同属性属于油层、气层、水层和干层的隶属度,作为定性参数定量化的依据。在建立的动态神经网络模型中,不同的储集层流体性质以10种输出元输出模式表示。在应用过程中可通过选择不同的输入参数和中间层神经元数目调整神经网络结构,以适应不同的地质情况和对实际资料的要求。通过在辽河油田曙光、大洼等多个地区的实际应用,系统取得了良好的应用效果。
Based upon the theories of artificial neural network and fuzzy mathematics,a computer system for integrated pay zone interpretation is developed on Sun workstation. There are 46 parameters used to recognize the mature of fluids in reservoir,such as data from core analysis,cuttings,sidewall coring,well log,gas log and drilling. The ownership to oil,gas,water or dry beds is calculated using fuzzy statistics for category attributes,which is a quantitative description for the attribute. Zones containing different fluids are characterized by ten output patterns in the developed dynamic neural network. The structure of the network is adjusted to fit different geologic cases and data obtained by changing the input parameters of the input layer and the number of nodes in the middle layer. The system was applied to some areas in Liaohe depression,and showed remarkable consistent results with well tests in the field..
Based upon the theories of artificial neural network and fuzzy mathematics,a computer system for integrated pay zone interpretation is developed on Sun workstation. There are 46 parameters used to recognize the mature of fluids in reservoir,such as data from core analysis,cuttings,sidewall coring,well log,gas log and drilling. The ownership to oil,gas,water or dry beds is calculated using fuzzy statistics for category attributes,which is a quantitative description for the attribute. Zones containing different fluids are characterized by ten output patterns in the developed dynamic neural network. The structure of the network is adjusted to fit different geologic cases and data obtained by changing the input parameters of the input layer and the number of nodes in the middle layer. The system was applied to some areas in Liaohe depression,and showed remarkable consistent results with well tests in the field..
引文
[1]周继成,周青山,韩飘扬.人工神经网络[M].北京:科学普及出版社,1993.
[2]黄玲,辜彬,刘玲.用人工神经网络综合测井和地震资料预测岩性[J].成都理工学院学报,1994,14(2):89-93.
[3]BaldwinJL,OtteDN,BatemanRM.ComputerEmulationofHumanMentalProcesses:ApplicationofNeuralNetworkSim-ulatorstoProblemsinWellLogPatternRecognition[A].Con-ferenceonArtificialIntelligenceinPetroleumExplorationandProduction[C].TexasA&MUniversity,1989.17-19.
[4]郭桂蓉.模糊模式识别[M].北京:国防科技大学出版社,1992.
[5]徐景祯,刘晓冬,高春文.人工神经网络模型在油气层识别技术中的应用[J].天然气工业,1996,16(4):22-24.
[6]XuJingzhen,LiuXiaodong.AnArtificialNeuralNetworkApproachinRecognitionofPayBedsandItsApplicationstoWestPartofSongliaoBasin[J].ScientiaGeologicaSinica,1996,5(2):261-266.
[2]黄玲,辜彬,刘玲.用人工神经网络综合测井和地震资料预测岩性[J].成都理工学院学报,1994,14(2):89-93.
[3]BaldwinJL,OtteDN,BatemanRM.ComputerEmulationofHumanMentalProcesses:ApplicationofNeuralNetworkSim-ulatorstoProblemsinWellLogPatternRecognition[A].Con-ferenceonArtificialIntelligenceinPetroleumExplorationandProduction[C].TexasA&MUniversity,1989.17-19.
[4]郭桂蓉.模糊模式识别[M].北京:国防科技大学出版社,1992.
[5]徐景祯,刘晓冬,高春文.人工神经网络模型在油气层识别技术中的应用[J].天然气工业,1996,16(4):22-24.
[6]XuJingzhen,LiuXiaodong.AnArtificialNeuralNetworkApproachinRecognitionofPayBedsandItsApplicationstoWestPartofSongliaoBasin[J].ScientiaGeologicaSinica,1996,5(2):261-266.
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