石油与天然气地质 ›› 2002, Vol. 23 ›› Issue (1): 76-80.doi: 10.11743/ogg20020116

• 油气空间 • 上一篇    下一篇

谢凤桥构造油气层的人工神经网络识别

杨久西1,2   

  1. 1. 中国地质大学研究生院, 湖北武汉, 430074;
    2. 中国石化新星公司中南石油局, 湖南长沙, 410117
  • 收稿日期:2001-12-09 出版日期:2002-03-25 发布日期:2012-01-16
  • 基金资助:

    湖北省自然科学基金资助;编号NO:99j007

IDENTIFICATION OF OIL HORIZONS BY ARTIFICIAL NEURAL NETWORKS IN XIEFENGQIAO STRUCTURE

Yang Jiuxi1,2   

  1. 1. The Graduation Institute of China University of Geosciences, Wuhan, Hubei;
    2. Zhongnan Petroleum Bureau, SINOPEC, Changsha, Hunan
  • Received:2001-12-09 Online:2002-03-25 Published:2012-01-16

摘要:

江汉盆地西南缘谢凤桥构造是一背斜构造,为岩性 +构造复合型油藏。油藏的聚集明显受储层纵向分布和横向展布及非均质性的控制。根据这一储层特点,用前向(BP)网络建立储层参数的仿真计算,然后用自组织映射特征(SOM)网络来预测油层类别。首先从一口或多口关键井所属已知数据中选取训练样本,选用冲洗带电阻率、真电阻率、自然伽马、自然电位、补偿声波、补偿中子及井径等 7类钻、测井数据作输入变量,由此建立测井数据参数与储层孔隙度、含油饱和度和渗透率等参数的输入输出映射关系。以产油井鄂深 4,8等井的已测试油层作为训练样本,用BP神经网络进行函数逼近,来预测储层参数。然后,利用SOM网络进行模式分类。将对储层较敏感的真电阻率、补偿声波进行二度输入,与BP网络所输出的孔隙度、含油饱和度、渗透率等储层参数一起作为油层识别的诊断特征参数。样本的特征参数经过标准化处理后,送入SOM网络进行学习训练和建模,得出建模样本油层识别的SOM网络,输入所要预测层位的数据,由网络仿真输出各井的油层识别结果。结果经生产检验,符合率超过 90%。

关键词: BP网络, SOM网络, 储层识别, 测井数据, 储层参数, 预测

Abstract:

Xiefengqiao anticlinal structure in southwestern margin of Jianghan Basin is a litho structural complex oil reservoir.Its oil accumulation was controlled by vertical and lateral distribution and heterogeneity of the reservoir.Two kinds of neural networks—forward network(BP) and self organizing feature mapping network(SOM)—were used in reservoir parameter simulated calculation and horizon type prediction respectively.Then chose the data of Esheng 4 and Esheng 8 Wells as training samples,used drilling and log data such as RXO,RT,GR,SP,AC,CNL and CAL as input variables to set up input/ouput mapping relation between log data and POR,So and K parameters of the reservoir,used BP network to make function approximation,used SOM network to make pattern sorting.Then two parameters RT and AC of the reservoirs were reinputted,the parameters,together with POR,So and oil saturation K outputted from BP networks were taken as characteristic parameters of the reservoir identification.The characteristic parameters were standared and trained by SOM networks so as to get model sample,then inputted the data of oil horizon that needed to be predicted,the result of oil horizon identification could be outputted from SOM networks.The validity is proved to be more than 90%.

Key words: BP network, SOM network, reservoir identification,logging data,reservoir parameter, prediction

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