四川盆地涪陵地区页岩裂缝测井定量识别

摘要

页岩气资源作为自生自储的非常规能源,页岩储层天然裂缝的发育情况不仅影响自身含气性,更对后期压裂改造效果有着直接影响.以四川盆地涪陵页岩气田五峰组—龙马溪组页岩储层宏观裂缝为主要研究对象,首先在探井岩心资料和电成像测井资料基础上,总结归纳了研究区页岩裂缝常规测井曲线响应特征,优选出能较好评价页岩储层裂缝的多种常规测井参数;然后通过分析电成像测井提供的裂缝信息,构建页岩裂缝定量计算模型;运用概率神经网络法,将探井的页岩裂缝定量计算结果与优选出的常规测井信息进行拟合,将拟合结果程序化,实现页岩裂缝的测井定量识别.通过探井页岩裂缝识别结果与该井电成像测井计算结果对比分析,认为该方法能较准确识别研究区页岩裂缝发育情况.研究区水平井页岩裂缝定量识别结果与页岩储层含气性和压裂施工参数的相关性研究,说明该方法能较好地评价页岩储层的含气性以及预测后期压裂改造效果.该研究对中国南方海相页岩气资源的进一步高效勘探开发有一定的参考意义.%Shale gas is considered as a kind of self-generation and self-preservation unconventional hydrocarbon resource.Both the shale gas content and fracturing performance are directly dependent on the fracture properties within shale reservoir.Based on the core test analysis and electric imaging logging data from exploration wells,the macro-fractures within Wufeng-Longmaxi Shale Formation in Fuling Shale Gas Field of Sichuan Basin are studied to summarize their conventional logging response patterns and optimize multiple conventional logging parameters for evaluation.A quantitative characterization model for shale fractures is established by the information of electrical imaging logging interpretation.The probabilistic neural network method is used to fit the quantitative fracture calculation with conventional logging information within the selected exploration wells.The fitting is programmed to realize the quantitative identification of shale fractures.A favorable correlation between the quantitative identification of horizontal-well shale fractures and shale gas content and fracturing operation parameters is provided,which indicates its validity for shale gas content and fracturing performance evaluation.This research could provide certain reference for the further efficient exploration and development of marine shale gas resource in southern China.