Abstract: A new method is introduced to predict the productivity of dual pore structure reservoir in carbonate rock profile from log data.First,some parameters to reflect reservoir productivity are extracted in terms of their geology and log characteristics.Considering the nonlinear relation between parameters and production capability and the varying rule of these data,BP artificial neural network is adopted to create the predicting mathematical model.And log data of many wells in LN oilfield are processed.The predicting productivity is rather accordant with the practical situation.All show that the result is better and this method is worth applying widely.

Key words: reservoirs productivity, dual pore reservoir, log and geology parametsrs, artificial neural network

摘要： 介绍了一种基于测井资料预测双孔结构碳酸盐岩储层油气产能的新方法.根据碳酸盐岩剖面中的双孔结构储层的地质和测井特征,提取与储层产能密切相关的多个测井和地质参数,考虑这些参数与产能的非线性相关关系以及产能数据的变化特点,采用BP神经网络技术建立其储层产能的预测模型.处理了轮南油田的多口井测井资料,所预测的储层段产能与试油产能较为一致,效果良好,值得推广应用.

关键词: 储层产能, 双孔结构储层, 测井和地质参数, 神经网络