五百梯气田长兴组储层裂缝发育程度的常规测井神经网络预测  

Prediction of Fracture Development in Changxing Formation Reservoir of Wubaiti Gas Field from Conventional Logging Data Using Neural Networks

作　　者：杨莉娜[1] 杨斌[1] 鲁洪江[1] 顾丽娜[1] 黄崇春[1] 

机构地区：[1]成都理工大学能源学院,四川省成都市610059

出　　处：《中国石油勘探》2011年第1期63-69,10,共7页China Petroleum Exploration

摘　　要：川东五百梯气田二叠系长兴组生物礁储层的基质孔隙度和渗透率较低,其高产的主控因素是裂缝的存在,使储层的渗透性有了提高。因此使用常规测井资料对单井裂缝发育程度进行判别预测就成为该地区研究工作的重点任务。通过提取岩心显示裂缝发育段的常规测井响应特征值作为样本,建立了储层裂缝发育程度的常规测井响应BP神经网络预测模型。通过该模型预测的裂缝总缝密度极大程度地与岩心观察相吻合,证明该模型适用于研究区,并且可以用来对该地区的单井裂缝发育程度进行判别预测。The reef reservoir in Permian Changxing Formation of Wubaiti gas field has relatively low matrix porosity and permeability,but the fractures in the reservoir lead to high yield and make the permeability higher.Therefore,the key task in the research of the area is to discriminate and predict the degree of fracture development in single well by using conventional logging data.Through breaking core,the conventional log response eigenvalue of the section where fractures developed is shown.By taking the conventional log response eigenvalue as the sample,the BP neural network model based on conventional log response is established to predict the degree of fracture development in the reservoir.The total fracture density predicted by using this model matches core observation to a great extent,which proves that this model is applicable for the study area and could be used to discriminate and predict the degree of fracture development in single well in the area.

关 键 词：常规测井 碳酸盐岩 裂缝 BP神经网络 FMI 裂缝总缝密度 五百梯气田 

分 类 号：P631.8[天文地球—地质矿产勘探]