网络出版日期: 2012-02-20
基金资助
国家科技重大专项“复杂油气田地质与提高采收率技术”(编号:2009ZX05009)部分成果
Prediction of minimum miscibility pressure in CO2 flooding based on general regression neural network
Online published: 2012-02-20
针对传统的最小混相压力预测方法应用不便或误差较大等问题,提出利用广义回归神经网络进 行CO2驱最小混相压力预测。以油藏温度、C5+分子量、中间组分摩尔分数、挥发组分摩尔分数为输入变 量,以最小混相压力为输出变量,建立广义回归神经网络预测模型,对CO2驱最小混相压力进行预测,将 结果与其他预测方法进行对比,并做误差分析。实例计算结果表明,广义回归神经网络用于CO2驱最小 混相压力预测是可行的,且具有精度高、收敛快、适用范围广、使用简便等特点。
关键词: 随机建模; 河流相; 实验设计; Monte-Carlo 计算; 不确定性分析
薛艳霞 , 廖新武 , 赵春明 , 霍春亮 , 张如才 . 基于广义回归神经网络的CO2 驱最小混相压力预测[J]. 岩性油气藏, 2012 , 24(1) : 108 -111 . DOI: 10.3969/j.issn.1673-8926.2012.01.021
To reduce the inconvenience and errors in traditional prediction methods for minimum miscibility pressure (MMP), a general regression neural network(GRNN) model was established for MMP prediction in CO2 flooding. The main factors affecting CO2 MMP, such as reservoir temperature, mole percentage of oil components (volatile and intermediate) and molecular weight of C5+ , were employed as the input variables of the GRNN, and the CO2 MMP was used as the output variable. To evaluate the advantage of the new method, the predicted results were compared between the GRNN model and the traditional empirical formula. The results show that the GRNN model is feasible for CO2 MMP prediction, and also has the characteristics of good precision, fast convergence, wide applicability and simple application.
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