【摘要】 河流相储层单砂体厚度小,砂体接触关系复杂,受限于地震分辨率的影响,对储层砂体的发育具有较大的不确定性。本文对工区储层的这种情况,采用了多种属性结合,利用属性优化方法,使用神经网络识别方法对有利砂体进行预测,并针对预测结果进行评价和检验,以期望获取更好的预测效果。更多还原

【Abstract】 Sand thickness in fluvial reservoir is thin, contact relationship is complex, as seismic resolution is limited, sand bodies in reservoir are uncertain. For this situation, uses seismic multi-attribute, attributes optimization method, neural network recognition method to predict favorable sand body, and evaluate and examine the results, expecting to obtain better prediction effect.更多还原