摘要
渗透率是储层评价中的重要参数,与传统的经验模型或统计模型计算的结果相比,BP神经网络由于高强度非线性映射能力及较强的自适应和自学能力,可以更精确地预测储层渗透率。通过对常规BP网络模型的改进,即在模型中加入定量化的岩性评价参数作为一个学习样本,建立了储层参数与测井响应及岩性之间的非线性模型。应用该方法对北部湾盆地涠西南凹陷涠洲某油田流一段的渗透率进行预测,取得了较好的效果。该方法计算的渗透率与实测渗透率吻合度很好,而且比用常规的、没有岩性控制的BP网络模型计算的渗透率精度更高。除了在储层参数预测方面进行应用,该方法还在储层沉积微相和岩性预测方面有着广泛的应用前景。
Permeability is one of the most important parameters in reservoir estimation.Compared with the calculated result by traditional experimental or statistical models,the BP neural net model can more accurately predict permeability because of its high nonlinear mapping ability and very strong abilities of self-adaptation and self-study.The present paper established a nonlinear model among reservoir property parameters,logging response and lithology by improving the conventional BP model,i.e.applying the quantitative lithology parameter to the BP model as a study sample.The permeability of the Liu-1 member of the Weizhou 11-7 oilfield in the Weixinan Depression,Beibuwan Basin was predicted by applying this method and the result was comparatively consistent well with the actually measured permeability,moreover,the precision of this method was much better than that applied by the conventional BP model without domination of lithology.Besides the application in the prediction of reservoir parameters,this method could be widely used in predicting reservoir microfacies and lithology as well.
出处
《石油学报》
EI
CAS
CSCD
北大核心
2010年第6期985-988,共4页
Acta Petrolei Sinica
基金
国家科技重大专项(2008ZX05023)资助
关键词
人工神经网络
BP算法
渗透率预测
岩性控制
储层物性
涠西南凹陷
artificial neural net
BP algorithm
permeability prediction
domination of lithology
reservoir properties
Weixinan Depression
