神经网络模式识别技术在井间储层参数预测中的应用
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摘要
用地震资料和少量测井资料预测储层参数的横向变化，是储层评价和油气勘探开发的重要研究课题。地震资料中蕴含着丰富的储层物性信息，若能建立起地震参数与储层物性参数之间的关系，则可利用地震资料进行储层参数的横向预测。文中提出了一种神经网络模式识别方法，用地震反射资料进行井间储层参数预测。应用结果表明，用网络法对井间孔隙度做出的估算值是准确的，用网络自动建立起来的非线性映射关系可能较好地反映井间孔隙度的分布。
It is an important problem in reservoir estimation and oil gas exploration and development to predict lateral variation of reservoir parameters from seismic data and well log. There is abundant reservoir information in seismic data. The relationship between seismic data and reservoir parameters can be used to estimate the reservoir parameters from seismic data. Neural network is a new information processing technique. It can be used to estimate reservoir parameters because of the capabilities in pattern recognition and complex nonlinear functions. This paper presents a neural network approach for estimation of crosswell reservoir parameters from seismic data. Application of neural network shows that this method is possible and available.
It is an important problem in reservoir estimation and oil gas exploration and development to predict lateral variation of reservoir parameters from seismic data and well log. There is abundant reservoir information in seismic data. The relationship between seismic data and reservoir parameters can be used to estimate the reservoir parameters from seismic data. Neural network is a new information processing technique. It can be used to estimate reservoir parameters because of the capabilities in pattern recognition and complex nonlinear functions. This paper presents a neural network approach for estimation of crosswell reservoir parameters from seismic data. Application of neural network shows that this method is possible and available.
引文
1李燕生．神经网络模式识别技术及其在井间储层参数预测中的应用［硕士论文］．北京：石油大学，19962焦李成．神经网络系统理论．西安电子科技大学出版社，19913HanDH，NurA．EfectofPorosityandClayContentonVelocitiesinSandstone．Geophysics，1986，51（11）：2093～21074边肇祺．模式识别．北京：清华大学出版社，19885陆基孟．地震勘探原理与资料解释．北京：石油工业出版社，1991
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