[1]张丽华,潘保芝,庄华,等.低孔隙度低渗透率储层压裂后产能测井预测方法研究[J].测井技术,2012,36(01):101-105.
 ZHANG Lihua,PAN Baozhi,ZHUANG Hua,et al.Productivity Log Forecasting Method for Postfrac Reservoir with Low Porosity and Low Permeability[J].WELL LOGGING TECHNOLOGY,2012,36(01):101-105.
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低孔隙度低渗透率储层压裂后产能测井预测方法研究()
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《测井技术》[ISSN:1004-1338/CN:61-1223/TE]

卷:
第36卷
期数:
2012年01期
页码:
101-105
栏目:
综合应用
出版日期:
2012-02-29

文章信息/Info

Title:
Productivity Log Forecasting Method for Postfrac Reservoir with Low Porosity and Low Permeability
作者:
张丽华1潘保芝1庄华1郭立新2李庆峰2赵小青2
1.吉林大学地球探测科学与技术学院, 吉林 长春 130026; 2.大庆钻探工程公司测井公司, 黑龙江 大庆 163412
Author(s):
ZHANG Lihua1PAN Baozhi 1ZHUANG Hua1GUO Lixin2LI Qingfeng2ZHAO Xiaoqing2
1.College of Geoexploration Science and Technology, Jilin University, Changchun, Jilin 130026, China; 2.Well Logging Company of Daqing Petroleum Administrative Bureau,Daqing, Heilongjiang 163412, China
关键词:
测井评价 产能预测 压裂 低孔隙度 低渗透率 产能级别 方程法 神经网络法
Keywords:
log evaluation productivity forecast fracturing low porosity low permeability productivity grade equation method neural network method
分类号:
P63184
摘要:
通过研究影响压裂后产能的各种参数,对低孔隙度低渗透率的朝阳沟油田进行了压裂后产能预测。对压裂后产能影响较大的参数有地层有效渗透率、有效厚度、井底流压等。确定了该区产能级别划分标准,提出2种预测方法:方程法和神经网络法。建立了朝阳沟油田预测压裂后产能的无限导流能力垂直裂缝稳态流方程,并给出了各参数的计算方法。应用方程法对11口井的压裂后产能进行预测,判别单层测试产油级别符合率为73%。应用神经网络法,选取研究区13口井进行训练和验证,单层测试产油级别符合率为100%。
Abstract:
Postfrac productivity forecasting in Chaoyanggou oilfield with low porosity and low permeability is made by means of researches on relative factors. The major factors which influence the postfrac productivity include: formation effective permeability, effective thickness, downhole fluid pressure, etc. Determined is the productivity level standard, and given are 2 prediction methods: equation method and neural network method. Built is the stable state flow equation method of infinite flow conductivity vertical fracture, and worked out is computing method for each parameter. Equation method is used to forecast the postfrac productivity in 11 wells, and productivity grade coincidence rate of single layer is up to 73%. When using neural network method to do training and prediction in 13 wells, the productivity grade coincidence rate reaches 100%.

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备注/Memo

备注/Memo:
收稿日期:2011-05-25
基金项目: 中央高校基本科研业务费专项资金项目(201103039)支持
作者简介: 张丽华,女,1974年生,博士,从事石油测井数据处理与解释方法研究工作。
文章编号:1004-1338(2012)01-0101-05
更新日期/Last Update: 2012-02-20