论文详情
基于三维地质建模的复杂断块致密气藏产能预测
断块油气田
2020年 27卷 第01期
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Title
Productivity prediction method of complex fault-block tight gas reservoir based on 3D geological model
作者
李金池
胡明毅
李忠诚
宋鹏
刘思琦
鞠秀叶3
单位
1.长江大学地球科学学院,湖北 武汉 430100
2.中国石油吉林油田分公司研究院,吉林 松原 138000
中国石化中原油田分公司濮东采油厂,河南 濮阳 457001
Organization
School of Geosciences, Yangtze University, Wuhan 430100, China
Jilin Oilfield Company Research Institute, PetroChina, Songyuan 138000, China
Pudong Oil Production Plant, Zhongyuan Oilfield Company, SINOPEC, Puyang 457001, China
摘要
气藏地质建模技术主要应用于气藏综合描述,能够定量分析孔隙度、渗透率及含气饱和度等储层物性参数的空间分布规律,而对于如何利用地质建模进行储层产能预测,没有经验可借鉴。为此,文中以小城子气田登娄库组复杂断块致密气藏为例,在气藏地质特征认识的基础上,从测井角度分析影响产能的敏感参数,并拟合产能方程,将地质、测井和试井三者相结合,综合应用含气砂体刻画、变方位角及分断块加权平均等建模方法建立气藏产能模型,实现了产能的综合评价。与实际试气、试采结果对比,该产能预测方法在气藏开发前期和中期具有较好的适用性和推广性。
Abstract
The geological modeling technology of gas reservoir is mainly used in the comprehensive description of gas reservoir, which can quantitatively analyze the spatial distribution of physical parameters of reservoir such as porosity, permeability and gas saturation. However, there is no experience in how to use geological modeling to predict reservoir productivity. Therefore, this paper takes Denglouku Formation complex fault block tight gas reservoir of Xiaochengzi gas field as an example, based on the understanding of the geological characteristics of the gas reservoir, analyzes the sensitive parameters that affect the productivity from the perspective of logging, and fits the productivity equation, combines the geology, logging and well testing, and comprehensively applies the modeling methods such as gas bearing sand body description, variable azimuth and weighted average of fault blocks to establish the productivity model of the gas reservoir. Compared with the actual gas test and production test results, the productivity prediction method has good applicability and popularization in the first and middle stage of gas reservoir development.
关键词:
致密气藏;
产能方程;
三维地质建模;
产能预测;
Keywords:
tight gas reservoir;
productivity equation;
3D geological modeling;
productivity prediction;
DOI
10.6056/dkyqt202001014