摘要
大牛地气田山西组一段储层的孔隙度和渗透率均低、孔隙结构复杂、非均质性强,具有低压、低产的特征。利用测井资料,采用人工神经网络技术对致密砂岩的岩性进行了识别,共识别出中—粗粒岩屑砂岩、岩屑石英砂岩两种岩性,结合三种孔隙度测井方法进行了孔隙度和渗透率的预测,利用岩电实验资料,建立了储层微观孔隙结构与胶结指数、饱和度指数、岩石弹性力学参数之间的统计关系,获得了有效的饱和度评价参数,提高了饱和度的解释精度。并以压汞、相渗等岩芯分析资料进行了束缚水饱和度的解释,并采用多种交绘图技术,有效地识别了致密砂岩气层。该方法的特点是充分放大测井信息对天然气的响应特征,增强了气层和干层的判别差异。实践表明,上述方法对于鄂尔多斯盆地大牛地气田致密砂岩气层测井评价的符合率达到95%以上。
Main lithology of tight gas sandstone reservoir in the first member of Shanxi Formation in (Daniu-) di gas pool is medium-grained and coarse-grained lithic sandstone and lithic quartzose sandstone.Sandstone reservoirs are characterized by low porosity and permeability,complicated pore structure,strong he-(terogeneity,)low pressure and low output.In order to apply logging data in the identification and evaluation,ANN technology is used to identify the lithology of tight sandstone.Three kinds of porosity logging are used to predict the porosity and permeability.While the lab data are used to build up the statistic relations between parameters,cement indicators,saturation indexes and elastic mechanics of the rocks.For this reason,effective parameters to get saturation can be obtained,and interpreted precision to saturation has been improved.Bound water saturation also has been evaluated on the basis of capillary data and relative permeability data.Finally four kinds of cross-plots have been used to identify gas pays effetively.The basic character of the set of methods is to magnify the log information response to gas and intensify the difference between gas layers and dry ones.In practice,a good logging evaluation result has been achieved in Daniudi gas pool, Ordos Basin,China.
出处
《地质科技情报》
CAS
CSCD
北大核心
2003年第4期65-70,共6页
Geological Science and Technology Information
基金
国家自然科学基金资助项目(49202031)
华北石油局规划设计研究院科研项目
