石油学报 ›› 2005, Vol. 26 ›› Issue (1): 65-68,73.DOI: 10.7623/syxb200501013

• 油田开发 • 上一篇    下一篇

应用储层随机建模方法计算概率储量

张明禄1, 王家华2, 卢涛1   

  1. 1. 长庆油田公司 陕西西安 710021;
    2. 西安石油大学 陕西西安 710065
  • 收稿日期:2004-03-19 修回日期:2004-06-01 出版日期:2005-01-25 发布日期:2010-05-21
  • 作者简介:张明禄,男,1963年9月生,1984年毕业于华东石油学院,长庆油田公司勘探开发研究院高级工程师,主要从事油气田开发研究和科研管理工作.E-mail:zml_cq@petrochina.com.cn
  • 基金资助:
    陕西省自然科学研究计划项目(98C28)"油气储层随机建模多个实现的分析及其在陕北安塞油田的应用研究"部分研究成果.

Calculation of probabilistic reserves of reservoir with stochastic modeling method

ZHANG Ming-lu1, WANG Jia-hua2, LU Tao1   

  1. 1. Changqing Oilfield Company, Xi'an, 710021, China;
    2. Xi'an Petroleum University, Xi'an, 710065, China
  • Received:2004-03-19 Revised:2004-06-01 Online:2005-01-25 Published:2010-05-21

摘要: 应用储层随机建模方法,研究了陕北地区气田的一个储层,分析了它的不确定性.分别利用了由254口、146口和30口井组成的3组数据,利用50、100、200个随机种子,计算了相应的天然气储量,分析了储量的概率分布.利用100个随机种子,分别求得了这3组数据概率储量P90,P50和P10,大于实际储量的概率分别是90%、50%和10%.计算结果表明,随着井数的增加,天然气储量分布的均值会不断地增加,而它们的均方差则减小,从而减小不确定性.利用克里金估计方法求取了地层的层面深度和有效厚度,利用高斯场模拟方法求取了孔隙度、渗透率、含气饱和度的空间分布.

关键词: 天然气储层, 随机建模, 物性参数, 概率储量

Abstract: The uncertainties of a gas reservoir located in the north part of Shaanxi Province were studied with stochastic modeling. The responding gas reserves of the reservoir were calculated with three groups of data obtained from two hundreds and fifty four wells, one hundred and forty six wells, thirty wells by using fifty, one hundred and two hundreds of random seeds, respectively. Probabilistic distributions of these reserves were analyzed. Three probabilistic reserves (P90, P50, P10) for the three groups of data were obtained respectively by using one hundred of random seeds. The probabilities of three probabilistic reserves larger than the real reserves are 90 percent, 50 percent and 10 percent respectively. The calculated results illustrate that as the number of wells increase, the mean values of gas reserves continuously increase, and their mean variances decrease, which results in the reduction of the uncertainties of reservoir. The top strata surface and net thickness were obtained by Kriging method. The spatial distributions of porosity, permeability and gas saturation were obtained by simulation of Gaussian field.

Key words: natural gas reservoir, stochastic modeling, physical parameter, probabilistic reserves

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