西南石油大学学报(自然科学版) ›› 2009, Vol. 31 ›› Issue (1): 116-120.DOI: 10.3863/j.issn.1674-5086.2009.01.028

• 石油与天然气工程 • 上一篇    下一篇

动态监测指标在预判区块开发中的应用研究

赵智勇1 刘道杰2 蒋华1 刘志斌2 申海华1   

  1. 1.中国石油大港油田测试公司,天津大港300280;2.西南石油大学理学院,四川成都610500
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-02-20 发布日期:2009-02-20

APPLICATION OF PERFORMANCE MONITORING INDICES IN PREDICTING DEVELOPMENT INDICES OF BLOCK

ZHAO Zhi-yong1 LIU Dao-jie2 JIANG Hua1 LIU Zhi-bin2 SHEN Hai-hua1   

  1. 1.Testing Company of PetroChina Dagang Oil Field Company,Dagang Tianjin 300280,China;2.School of Science,Southwest Petroleum University,Chengdu Sichuan 610500,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-02-20 Published:2009-02-20

摘要:

通过对油田实际生产环节深入的研究,建立了区块监测开发指标总体系,引入多输入多输出系统理论,将区块监测指标特征作为系统输入,区块开发指标作为系统输出,用神经网络方法及微分模拟方法,基于历史数据实现系统输入输出的功能模拟,建立起区块监测指标特征与区块开发指标的关联关系,对区块监测指标特征进行统计趋势推断,以此为基础通过关联模型预测区块开发动态指标,由预测的区块开发指标对开发形势进行预判。实例分析表明,该方法在开发形势预判中具有较好的效果。

关键词: 区块监测指标特征, 开发指标, 功能模拟, 关联模型, 预判

Abstract:

A block system for monitoring indices and development indices is established by the thorough research on practical oil production process.At the first,based on the multi-input and multi-output system theory,the correlation between monitoring indices and development indices of blocks is established by the methods of neural network and the differential simulation where the monitoring indices of blocks are viewed as system input and development indices of blocks as system output,and then,the input-output functional simulation of the monitoring system is realized according to historical data and statistical tendency inference to the sub-area monitoring indices characteristic.Furthermore,the monitoring system is used to forecast the development performance indicators of blocks and provide a basis for study indicates that it is a very practical method to predict block development indices by block monitoring indices and good effects are obtained on practical application to oil-field development.

Key words: block monitoring indices characteristic, development indices, function simulation, correlation model, predict

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