基于模糊C均值聚类与Bayes判别的致密油储层分类评价

王伟; 康胜松; 高峰; 郭粉转; 张亮

doi:10.3969/j.issn.1006-6535.2020.05.018

特种油气藏 >

2020 , Vol. 27 >Issue 5: 118 - 124

DOI: https://doi.org/10.3969/j.issn.1006-6535.2020.05.018

油藏工程

基于模糊C均值聚类与Bayes判别的致密油储层分类评价

王伟 ,
康胜松 ,
高峰 ,
郭粉转 ,
张亮

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陕西延长石油(集团)有限责任公司,陕西西安 710075

王伟(1983—),男,工程师,2007年毕业于长江大学地理信息系统专业,2012年毕业于该校矿产普查与勘探专业,获硕士学位,现从事油田开发地质工作。

收稿日期: 2019-12-18

修回日期: 2020-07-08

网络出版日期: 2022-02-18

基金资助

陕西省科技统筹创新项目“延长难采储量有效动用开发技术研究”(2016KTCL01-02)

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Classification and Evaluation of Tight Oil Reservoirs Based on Fuzzy C-Means Clustering and Bayes Discrimination

Wang Wei ,
Kang Shengsong ,
Gao Feng ,
Guo Fenzhuan ,
Zhang Liang

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Shaanxi Yanchang Petroleum (Group) Co., Ltd., Xi'an Shaanxi 710075,China

Received date: 2019-12-18

Revised date: 2020-07-08

Online published: 2022-02-18

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摘要

为解决常规致密油储层因缺乏岩心分析资料而难以进行储层分类评价的难题,通过优选属性参数,应用模糊C均值算法划分致密油储层最优类别,再运用Bayes判别分析法建立储层类别与常规测井属性的关系式,利用常规采油井的测井属性判别储层的类别。现场实践应用表明:利用该方法划分的鄂尔多斯盆地志丹地区长₇Ⅱ⁶、长₇Ⅰ²射孔砂体储层类别与试油结果一致,研究区203口井储层划分吻合率为89.7%。该研究对高效开发致密油藏具有一定的指导意义。

关键词： 致密油; 储层分类; 模糊C均值聚类; Bayes判别; 储层评价; 鄂尔多斯盆地

本文引用格式

王伟 , 康胜松 , 高峰 , 郭粉转 , 张亮 . 基于模糊C均值聚类与Bayes判别的致密油储层分类评价[J]. 特种油气藏, 2020 , 27(5) : 118 -124 . DOI: 10.3969/j.issn.1006-6535.2020.05.018

Abstract

In order to solve the problem that the conventional tight oil reservoirs are difficult to classify and evaluate due to the lack of core analysis data, the optimal type of tight oil reservoirs was classified by fuzzy C-means algorithm through optimizing attribute parameters; then Bayes discriminant analysis was applied to establish the relationship between reservoir types and conventional well logging attributes, and the well logging attributes of conventional oil production wells were utilized to distinguish the reservoir types. Field applications show that the reservoir types of Chang₇ II⁶ and Chang₇ I² perforated sand bodies in Zhidan area of the Ordos Basin are consistent with the oil test results. The coincidence rate of reservoir classification of 203 wells in the study area is 89.7%. This study has a certain guiding significance for the efficient development of tight oil reservoirs.

Key words： tight oil; reservoir classification; fuzzy C-means clustering; Bayes discrimination; reservoir evaluation; Ordos Basin

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