石油与天然气地质 ›› 2012, Vol. 33 ›› Issue (1): 94-100,110.doi: 10.11743/ogg20120112

• 储层与层序 • 上一篇    下一篇

应用实验设计优选随机建模的储层模型

崇仁杰1,2, 于兴河1, 李婷婷3   

  1. 1. 中国地质大学 能源学院, 北京100083;
    2. BP中国勘探及生产公司, 北京100020;
    3. 中国石油 吉林油田分公司 新立采油厂, 吉林 松原 138000
  • 收稿日期:2010-12-02 修回日期:2011-12-27 出版日期:2012-02-28 发布日期:2012-03-22

Application of experimental design theory in stochastic reservoir model optimization

Chong Renjie1,2, Yu Xinghe1, Li Tingting3   

  1. 1. School of Energy Resources, China University of Geoscience, Beijing 100083, China;
    2. BP China E&P, Beijing 100020, China;
    3. Xinli Oil Production Department, PetroChina Jilin Oilfield Company, Songyuan, Jilin 138000, China
  • Received:2010-12-02 Revised:2011-12-27 Online:2012-02-28 Published:2012-03-22

PDF (PC)

1184

被引次数

16

摘要：

针对目前常用模型优选方法存在的主要问题,提出了一种将实验设计的理念应用于不确定性储层模型优选的方法。首先,根据油田的实际情况确定影响模型地质储量的不确定性变量及其可能变化范围,并采用实验设计确定建模方案;然后,建立多个确定模型并计算储量,对不确定变量与对应模型的地质储量进行多元回归;最终,应用蒙特卡罗模拟技术确定概率储量的P10,P50和P90模型的参数,并建立P10,P50和P90模型。该方法在应用过程中不仅更加客观、省时,而且,这种方法可以确定影响地质储量的主要地质变量,为评价地质储量的不确定性提供了一个全新的思路。

关键词: 不确定性评价, 实验设计, 随机建模, 概率储量, 模型优选, 储层模型, 储量评价

Abstract:

To improve the conventional reservoir model optimization techniques,this paper presents a method to introduce experimental design theory to uncertainty reservoir modeling optimization.First,we identified the dominant nondeterministic variables that influence the OOIP calculation and their probability distributions by the actual oilfield data analysis,and made a modeling plan through experimental design.And then,several deterministic models were built to calculate OOIP.Multiple regression was performed on the nondeterministic variables and the OOIP calculated with corresponding models.Finally,the Monte Carlo simulation was run to obtain the OOIP probability curve and P10,P50 and P90 model parameters,which were used to establish the reference P10,P50 and P90 models.This method is not only more objective and efficient,but also capable of identifying the key geologic uncertainties that influence the OOIP calculation and provides a new way to evaluate the uncertainty of OOIP.

Key words: uncertainty assessment, experimental design, stochastic modeling, probabilistic reserve, model optimization, reservoir model, reserve assessment

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