基于样式降维聚类的多点地质统计建模算法

doi:10.7623/syxb201611008

石油学报 ›› 2016, Vol. 37 ›› Issue (11): 1403-1409.DOI: 10.7623/syxb201611008

基于样式降维聚类的多点地质统计建模算法

喻思羽¹, 李少华^1,2, 何幼斌¹, 段太忠³, 廉培庆³, 陶金雨¹, 包兴¹

1. 长江大学地球科学学院湖北武汉 430100;
2. 非常规油气湖北省协同创新中心湖北武汉 430100;
3. 中国石油化工股份有限公司石油勘探开发研究院北京 100083

收稿日期:2016-01-04 修回日期:2016-07-25 出版日期:2016-11-25 发布日期:2016-12-10
通讯作者: 李少华,男,1972年8月生,1994年获江汉石油学院学士学位,2003年获中国石油勘探开发研究院博士学位,现为长江大学教授、博士生导师,主要从事地质统计学、储层建模方面的教学与科研工作。Email:534354156@qq.com
作者简介:喻思羽,男,1987年6月生,2009年获长江大学学士学位,现为长江大学地球科学学院博士研究生,主要从事地质统计建模与算法研究。Email:573315294@qq.com
基金资助:
国家自然科学基金项目（No.41272136，No.41572121）、国家重大科技专项（2016ZX05033-003-007）和湖北省科技创新群体项目“储层精细表征与建模”（2016CFA024）资助。

Multiple-point geostatistics algorithm based on pattern scale-down cluster

Yu Siyu¹, Li Shaohua^1,2, He Youbin¹, Duan Taizhong³, Lian Peiqing³, Tao Jinyu¹, Bao Xing¹

1. College of Geosciences, Yangtze University, Hubei Wuhan 430100, China;
2. Hubei Cooperative Innovation Center of Uncoventional Oil and Gas, Hubei Wuhan 430100, China;
3. Sinopec Petroleum Exploration and Production Research Institute, Beijing 100083, China

Received:2016-01-04 Revised:2016-07-25 Online:2016-11-25 Published:2016-12-10

摘要/Abstract

摘要：

评价多点地质统计建模算法的一个重要指标是能否在保证建模质量的同时较好地协调计算效率和内存空间二者的平衡。基于样式的多点地质统计建模算法SIMPAT存在一定不足，使得SIMPAT算法在提出多年后仍然难以实际应用。国外学者提出了SIMPAT算法的改进算法：Filtersim和DisPat，但是在平衡计算效率和内存空间问题上仍存在一定的不足。通过深入剖析SIMPAT算法的原理和特点提出了基于样式降维聚类的多点地质统计建模算法。新算法采用邻近等间距取样法对所有数据样式进行降维聚类处理，把相似的数据样式聚为一类。不同于SIMPAT算法的一次相似度匹配计算，新算法采用2次相似度比较：先比较数据事件与样式类代表性样式的相似度，找到最相似的样式类；再进一步比较数据事件与该样式类中全部数据样式的相似度，进而确定相似度最大的数据样式。以二维和三维实例比较了新算法与传统多点地质统计建模算法SIMPAT、Snesim、Filtersim和DisPat在相同参数条件下的模拟计算效率。结果表明，新算法在保证模拟质量基础上极大提高了基于样式的多点地质统计建模算法的计算效率，并节省内存空间。

关键词: 多点地质统计学, 聚类, SIMPAT, 数据样式, 计算效率

Abstract:

A key evaluation indicator of multiple-point geostatistics modeling algorithm is to ensure modeling quality when harmonizing the equilibrium between computational efficiency and memory space. Simulation of Patterns (SIMPAT) is a pattern-based multiple-point geostatistics algorithm for geological modeling, leading to a low computational efficiency of SIMPAT. Thus, SIMPAT algorithm is still difficult for practical application since proposed many years ago. The improved algorithms of SIMPAT were put forward by foreign scholars, including Filtersim and DisPat. However, there are still certain defects in harmonizing the equilibrium between computational efficiency and memory space. Through deeply studying the theory and characteristics of SIMPAT algorithm, multiple-point geostatistics (MPS) algorithm based on pattern scale-down cluster was proposed. In the new algorithm, the adjacent spacing sampling method is used to perform scale-down clustering on all data patterns, so as to cluster similar data patterns into one category. Different from the one-step similarity match calculation of SIMPAT, two-step similarity contrast is adopted in the new algorithm. Firstly, a comparison is conducted on the similarities between data event and representative pattern, so as to find the most similar pattern cluster. Further, a comparison is made on the similarities between data event and the whole data pattern of such pattern cluster, thus determining the data pattern with maximum similarity. Based on 2D and 3D cases, this study also makes a comparison between the simulation computation efficiency of the new algorithm and the traditional MPS, such as SIMPAT, Snesim, Filtersim and DisPat under the same parameter conditions. The results indicate that the new algorithm can greatly improve the computational efficiency of pattern-based MPS algorithm on the basis of simulation quality assurance and also save memory spaces.

Key words: multiple-point geostatistics, clustering, SIMPAT, data pattern, computation efficiency

中图分类号:

TE19

喻思羽, 李少华, 何幼斌, 段太忠, 廉培庆, 陶金雨, 包兴. 基于样式降维聚类的多点地质统计建模算法[J]. 石油学报, 2016, 37(11): 1403-1409.

Yu Siyu, Li Shaohua, He Youbin, Duan Taizhong, Lian Peiqing, Tao Jinyu, Bao Xing. Multiple-point geostatistics algorithm based on pattern scale-down cluster[J]. Acta Petrolei Sinica, 2016, 37(11): 1403-1409.

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基于样式降维聚类的多点地质统计建模算法

Multiple-point geostatistics algorithm based on pattern scale-down cluster

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