基于储层分类的低孔隙度低渗透率储层产能预测方法研究

On Productivity Prediction of Low Porosity and Permeability Reservoirs Based on Reservoirs Classification

利用研究区内22块压汞样品及4 440块有效物性分析样品,建立起低孔隙度低渗透率储层的分类标准,为基于测井数据进行储层分类提供参考依据与标准。依据标准对253个储层进行了类别划分,优选了用于储层类别划分的测井特征参数,建立了3类储层特征参数分布范围及均值。借助自适应BP神经网络技术建立了适合研究区长6_3段的储层分类判别模型,并将其判别结果与综合判别结果对比,吻合程度较高。通过储层指数与产能关系分析,储层指数与储层产能之间具有较好的相关性。利用储层指数建立的产能预测模型具有较好的应用效果。

Based on 22 mercury injection samples and 4 440 effective physical properties analysis samples, established is a classification standard of low porosity and permeability reservoirs. This is the classification standard based on log data. According to this classification standard, the 253 reservoirs are classified, the log characteristic parameters of reservoirs classification are selected, the range and the average value of the parameters on 3 types of reservoirs are established. Meanwhile self-adapted neural networks technology is used to establish reservoir classification model in Chang 63 block, and its results are close to the comprehensive analysis results. Through analyzing the relation between the reservoirs index and productivity of hydrocarbon reservoirs, there is a well correlation between the reservoirs index and the productivity. This model of productivity prediction based on reservoirs classification has been successfully applied to Chang 63 block, Baibao area.