郭富欣,王亚青,尚凡杰,房磊,张新叶
GUO Fuxin,WANG Yaqing,SHANG Fanjie,FANG Lei,ZHANG Xinye

• 1:中海油研究总院

摘要(Abstract)：

为进行油藏数值模拟,需要先对细网格地质模型的属性进行粗化,粗化过程会产生信息损失,为最大程度地减小粗化过程中的地质信息的损失,需要对现有的粗化方法进行筛选。通过文献调研,深入分析了目前常用的渗透率粗化方法的粗化原理,并对比了不同粗化方法的优缺点,发现不同粗化方法适用条件不同,并且不同粗化方法对于属性粗化结果的影响不同。以Libra油田为例进行了机理模型研究,利用不同粗化方法对细模型渗透率分别进行粗化,并对细模型及各粗化后的模型分别进行数值模拟。选取含水率和日产油率作为对比参数,通过对数值模拟结果的对比进行了粗化方法优选。最后确定最适合研究区的粗化方法为流动法,其粗化结果更接近细模型。
In order to carry out oil reservoir numerical simulation, the attributes of the fine geological model must be upscaled first, the upscaling may bring the information lost. So as to minimize the loss, it is necessary to choose the existing upscaling methods. With the help of the literature investigation, an in-depth analysis of the calculating principle was made for the commonly used permeability upscaling methods. Meanwhile both the advantages and disadvantages of different upscaling methods were compared. The comparison showed that different upscaling methods were suitable for different conditions and moreover those methods had different influences on the upscaled results. Taking Libra Oilfield as an example, the mechanism model was studied. Different upscaling methods were used to upscale the permeability of the fine geological model, at the same time, the numerical simulations were separately conducted for the fine model and each upscaled model. The water cut and daily oil production rate were selected as the comparing parameters, the optimization of the upscaling methods was engaged by means of the numerical simulation results. Finally the flow method was determined to be the most suitable upscaling method for the study area; its upscaled result was more close to that of the fine model.

关键词(KeyWords)： 渗透率;粗化原理;方法优选;机理模型;巴西Libra油田;质量守恒
permeability;upscaling/coarsening principle;method optimization;mechanism model;Libra oilfield in Brazil;conservation of mass

Abstract:

Keywords:

基金项目(Foundation): 国家科技重大专项“海外重点油气田开发钻采关键技术”(2017ZX05032-004)

作者(Author): 郭富欣,王亚青,尚凡杰,房磊,张新叶
GUO Fuxin,WANG Yaqing,SHANG Fanjie,FANG Lei,ZHANG Xinye

DOI: 10.19597/j.issn.1000-3754.201808010

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