西湖凹陷中央背斜带中北部花港组储层成岩相测井识别

基于测井资料对未取心井区域的成岩相进行人工智能识别成为必然的趋势。利用岩心铸体薄片与扫描电镜资料,划分西湖凹陷中央背斜带中北部花港组储层成岩相,总结了六种典型成岩相类型,各类成岩相所对应的测井响应差异明显。结果表明,测井系列自然伽马、补偿中子、声波时差、深侧向电阻率、浅侧向电阻率和密度等为成岩相判别敏感曲线。对比岩心薄片资料表明,概率神经网络模型可以准确识别未取心井段的成岩相,识别准确率可达到90%以上。

Reservoir diagenetic facies logging identification of Huagang formation in central-northern of the central anticlinal belt of Xihu sag

Artificial intelligence recognition of diagenetic facies in the area without coring wells based on logging data has become an inevitable trend. The diagenetic facies of Huagang formation reservoir in the north-central anticline zone of Xihu sag are divided by using core casting thin sections and scanning electron microscopy data and six kinds of typical diagenetic facies types are summarized. The corresponding logging responses of different diagenetic facies are obviously different. The results show that the log series of natural gamma ray, compensated neutron, acoustic time difference, deep lateral resistivity, shallow lateral resistivity and density are sensitive curves for diagenetic facies discrimination. Comparing the slice data of cores, it is shown that the probabilistic neural network model can accurately identify the diagenetic facies of the uncored interval, and the recognition rate can reach more than 90%.