新疆石油地质

新疆石油地质 ›› 2011, Vol. 32 ›› Issue (6): 653-655.

• 应用技术 • 上一篇    下一篇

基于BP 神经网络的石油运聚系数预测模型

吕一兵1, 张涛1, 吕修祥2   

  1. 1.长江大学信息与数学学院,湖北荆州 434023;
    2.中国石油大学盆地与油藏研究中心,北京 102249
  • 收稿日期:2011-01-04 出版日期:2011-12-01 发布日期:2020-08-20
  • 作者简介:吕一兵（1979-）,男,湖北钟祥人,博士,人工智能、系统决策,（Tel）15872138969（E-mail）Lvyibing_2001@sohu.com.
  • 基金资助:
    国家自然科学基金项目（10926168,41072102）; 国家专项（2009GYX02-03）

Forecast Model for Oil Migration and Accumulation Coefficient Based on BP Neural Network

LV Yi-bing1, ZHANG Tao1, LV Xiu-xiang2   

  1. 1. School of Information and Mathematics, Yangtze University, Jingzhou, Hubei 434023, China;
    2. Research Center of Basin and Reservoir, China University of Petroleum, Beijing 102249, China
  • Received:2011-01-04 Online:2011-12-01 Published:2020-08-20

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摘要： 根据一系列石油运聚单元的解剖结果,筛选出影响石油运聚系数的主控地质因素。建立了以主控地质因素为输入向量,运聚系数为输出向量的BP 神经网络石油运聚系数预测模型。应用结果表明,所建立的BP 神经网络模型具有较好的预测效果,平均相对误差为10.89%,有效性指数达到92.51%,且运聚系数预测精度高于多元非线性回归模型的预测。

关键词: 运聚系数, BP 神经网络, 模型, 预测

Abstract: Based on the data from a series of petroleum migration and accumulation units, the major geologic factors for controlling oil migration and accumulation coefficient are selected. The forecast model for oil migration and accumulation coefficient based on BP neural network is developed by taking the major geologic factors as the input vectors and the oil migration and accumulation coefficients as output vectors. It is indicated that the applied result of this model is in good agreement with the observed data with average relative error of 10.89% and the corresponding agreement index is about 92.51%. Moreover, the prediction precision by using this model is much higher than that by using multi-element nonlinear regression model.

Key words: migration and accumulation coefficient, BP neural network, model, forecast, prediction

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