Integrative prediction of microfacies with multiple stochastic modeling methods

doi:10.7623/syxb200602014

Editorial office of ACTA PETROLEI SINICA ›› 2006, Vol. 27 ›› Issue (2): 68-71.DOI: 10.7623/syxb200602014

• Oil Field Development • Previous Articles Next Articles

Integrative prediction of microfacies with multiple stochastic modeling methods

Yin Yanshu¹, Wu Shenghe¹, Zhang Changmin², Li Shaohua², Zhang Shangfeng²

1. Faculty of Resources and Information Technology, China University of Petroleum, Beijing 102249, China;
2. Department of Earth Science, Yangtze University, Jingzhou 434023, China

Received:2005-05-08 Revised:2005-06-20 Online:2006-03-25 Published:2010-05-21

用多种随机建模方法综合预测储层微相

尹艳树¹, 吴胜和¹, 张昌民², 李少华², 张尚锋²

1. 中国石油大学资源与信息学院, 北京, 102249;
2. 长江大学地球科学系, 湖北, 荆州, 434023

作者简介:尹艳树,男,1978年8月生,2000年毕业于江汉石油学院,现为中国石油大学(北京)在读博士研究生,主要从事油藏描述与储层建模研究.E-mail:yys6587@sina.com
基金资助:
国家重点基础研究发展规划(973)项目(2001CB209106)“低效气藏的形成机理及地质模式研究”;中国石化集团公司科技攻关项目(P031033)“深层低渗难动用储量开发配套技术”部分成果

Abstract

Abstract: A new comprehensive method for predicting reservoir microfacies by integrating multiple stochastic modeling methods was presented.The high-resolution reservoir microfacies model was constructed with the object-based methods which can show the crisp shape of microfacies and with the pixel-based methods which can reproduce the complex distribution of microfacies.A high-precision reservoir geological model was established,and a general modeling flowchart was given.Taking the subaqueous fan reservoir of Pucheng Oilfield in Dongpu Depression as an example,the object-based stochastic modeling method was integrated with the pixel-based sequential indicator stochastic modeling method to construct the microfacies distribution model of Es₃² in Pucheng Oilfield.The modeling result is well matched with the real distribution of the reservoir microfacies of Pucheng Oilfield.

Key words: reservoir geological model, stochastic modeling method, sedimentary microfacies, reservoir prediction, Pucheng Oilfield

摘要： 提出了将多种随机建模方法综合预测储层微相的新方法.该方法是利用基于目标的方法较好地再现了简单几何形态目标分布的特点;基于象元的方法再现了复杂空间展布目标的特点.建立了反映实际储层微相分布的高精度储层地质模型,给出了具体建模流程.以濮城油田水下扇储层为例,将基于目标的储层目标层次建模方法与基于象元的序贯指示建模方法相结合,从而获得濮城油田沙三中亚段微相储层地质模型.模拟结果检验表明,建模效果较好.

关键词: 储层地质模型, 随机建模方法, 沉积微相, 储层预测, 濮城油田

CLC Number:

TE319

Yin Yanshu, Wu Shenghe, Zhang Changmin, Li Shaohua, Zhang Shangfeng. Integrative prediction of microfacies with multiple stochastic modeling methods[J]. Editorial office of ACTA PETROLEI SINICA, 2006, 27(2): 68-71.

尹艳树, 吴胜和, 张昌民, 李少华, 张尚锋. 用多种随机建模方法综合预测储层微相[J]. 石油学报, 2006, 27(2): 68-71.

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Integrative prediction of microfacies with multiple stochastic modeling methods

用多种随机建模方法综合预测储层微相

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