摘要
在油气勘探开发研究中,储层分类是一个重要的环节,当前,随着非常规油气藏开发的不断深入研究,致密砂岩气藏的开发技术日趋成熟。这类气藏通常是低孔低渗储层,因此,研究低孔低渗储层对于了解致密砂岩气藏至关重要。本文结合宁夏青石峁地区石盒子组致密砂岩储层的特点,选取孔隙度、渗透率、流动单元因子作为储层特征参数,为了使分类的储层更具有代表性且更准确,在进行储层分类前,先对所选取的特征参数表进行数据的异常识别,再通过K-means聚类分析方法得出针对青石峁地区的储层分类标准,最后进行基于决策树方法的储层类型识别。由此得出的结论符合致密砂岩低孔低渗的特点,并且结合其试气资料可以在极大程度上降低开采成本,提高产能。
In the research of oil and gas exploration and development,reservoir classification is an important link.Currently,with the continuous in-depth study of unconventional oil and gas reservoir development,the development technology of tight sandstone gas reservoirs is becoming increasingly mature.This type of gas reservoir is usually a low porosity and low permeability reservoir,so studying low porosity and low permeability reservoirs is crucial for understanding tight sandstone gas reservoirs.This article combines the characteristics of tight sandstone reservoirs in the Shihezi formation of the Qingshimao area in Ningxia,and selects porosity,permeability,and flow unit factors as reservoir characteristic parameters.In order to make the classified reservoirs more representative and accurate,before conducting reservoir classification,the selected characteristic parameter table is first used to identify anomalies in the data.Then,the K-means clustering analysis method is used to obtain the reservoir classification standards for the Qingshimao area,and finally,the decision tree method is used to identify reservoir types.The conclusion drawn from this is in line with the characteristics of low porosity and low permeability of tight sandstone,and combined with its gas testing data,it can greatly reduce production costs and improve production capacity.
作者
陈龙
嵇雯
王舵
高建英
田立立
CHEN Long;JI Wen;WANG Duo;GAO Jianying;TIAN Lili(School of Earth Science and Engineering,Xi'an Shiyou University,Xi'an Shaanxi710065,China;Shaanxi Key Laboratory of Petroleum Accumulation Geology,Xi'anShiyou University,Xi'an Shaanxi 710065,China;Research Institute of Explorationand Development,PetroChina Changqing Oilfield Company,Xi'an Shaanxi 710018,China;National Engineering Laboratory for Exploration and Development ofLow-Permeability Oil&Gas Field,Xi'an Shaanxi 710018,China)
出处
《石油化工应用》
CAS
2024年第10期50-54,67,共6页
Petrochemical Industry Application
关键词
致密砂岩储层
异常识别
K-means均值算法
决策树
tight sandstone reservoir
abnormal identification
K-means clustering algorit-hm
decision tree
