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物探与化探  2020, Vol. 44 Issue (5): 1190-1200    DOI: 10.11720/wtyht.2020.1561
  方法研究·信息处理·仪器研制 本期目录 | 过刊浏览 | 高级检索 |
基于灰色系统与测井方法的煤层气含量预测及应用
郭建宏1,2(), 张占松1,2(), 张超谟1,2, 陈芷若2, 张鹏浩1,2, 汤潇1,2, 秦瑞宝3, 余杰3
1.长江大学 物理与石油资源学院,湖北 武汉 430100
2.长江大学 油气资源与勘探技术教育部重点实验室,湖北 武汉 430100
3.中海油研究总院,北京 100027
Prediction and application of coalbed methane content based on gray system and logging method
GUO Jian-Hong1,2(), ZHANG Zhan-Song1,2(), ZHANG Chao-Mo1,2, CHEN Zhi-Ruo2, ZHANG Peng-Hao1,2, TANG Xiao1,2, QIN Rui-bao3, YU Jie3
1.College of Physics and Petroleum Resources, Yangtze University, Wuhan 430100, China
2.Key Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan 430100, China
3.CNOOC Research Institute, Beijing 100027, China
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摘要 

煤层气含量是评价煤储层的一个重要参数。本文将灰色系统用于煤层测井曲线,利用改进的斜率关联度法,分析了对煤层气含量敏感的测井曲线序列;对正关联相关的测井曲线序列利用灰色多变量静态模型GM(0,N)预测煤层气含量。并以沁水煤田为例,将预测结果与多元回归模型分析的结果进行比较并对本文方法模型的实用性进行研究分析。结果表明,应用改进的斜率关联度对测井曲线与煤层气含量进行灰色关联分析能更充分开发测井曲线与煤层气含量的关系;用GM(0,N)模型预测煤层气含量比多元回归模型预测的结果更精确,且本文模型更为强健,可在样本数据相对较少的情况下有效预测煤层气含量曲线,结果可信度高,具有实际应用价值。

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作者相关文章
郭建宏
张占松
张超谟
陈芷若
张鹏浩
汤潇
秦瑞宝
余杰
关键词 煤层气含量测井曲线灰色关联分析GM(0,N)预测模型    
Abstract

The content of coalbed methane is an important parameter in evaluating coalbed reservoir. In this paper, the gray system was applied to the coalbed logging curve, the improved slope correlation method was used to analyze the logging curve series which are sensitive to the coalbed gas content. The gray multivariate static model GM (0,N) was used to predict the coalbed methane content in the sequence of positive correlation logging curves. Taking Qinshui Coal Field as an example, the authors compared the gray multivariate static model prediction results with the results of the multiple regression model analysis, and studied and analyzed the practicability of the gray multivariate static model. The results show that the improved association analysis of gray incidence can fully develop the relationship between logging curve and coalbed methane content, and that the GM(0,N) prediction model is more accurate and more robust than the multiple regression model in that it can effectively predict the coalbed methane content curve when the sample data is relatively small. The result is reliable and has practical application value.

Key wordscoalbed methane content    logging curve    association analysis of gray incidence    GM(0,N) prediction model
收稿日期: 2019-12-02      出版日期: 2020-10-26
:  P631  
基金资助:国家科技重大专项(2016ZX05060001-012);湖北高校省级大学生创新训练项目(201810489075)
通讯作者: 张占松
作者简介: 郭建宏(1997-),男,山东招远人,主要研究方向测井方法与解释、煤层气测井智能评价。Email: 87942024@qq.com
引用本文:   
郭建宏, 张占松, 张超谟, 陈芷若, 张鹏浩, 汤潇, 秦瑞宝, 余杰. 基于灰色系统与测井方法的煤层气含量预测及应用[J]. 物探与化探, 2020, 44(5): 1190-1200.
GUO Jian-Hong, ZHANG Zhan-Song, ZHANG Chao-Mo, CHEN Zhi-Ruo, ZHANG Peng-Hao, TANG Xiao, QIN Rui-bao, YU Jie. Prediction and application of coalbed methane content based on gray system and logging method. Geophysical and Geochemical Exploration, 2020, 44(5): 1190-1200.
链接本文:  
https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2020.1561      或      https://www.wutanyuhuatan.com/CN/Y2020/V44/I5/1190
样号 测试气量
/(m3·t-1)
深度曲线
/m
自然伽马
/API
自然电位
/mV
补偿密度
/(g·cm-3)
声波时差
/(μs·m-1)
补偿中子
/(V·V-1)
深电阻率
/(Ω·m)
浅电阻率
/(Ω·m)
1 18.58 974.26 37.5 65 1.41 411 0.50 8174 5524
2 16.59 974.50 50.4 55 1.47 413 0.51 7594 5225
3 16.99 976.99 49.1 28 1.25 414 0.47 1620 1641
$\vdots$
40 17.17 1239.27 24.0 83 1.54 443 0.46 159 237
Table 1  15号煤层测试含气量与测井标准化参数
Fig.1  煤储层含气量与测井参数之间的关系
R12
自然伽马
R22
深电阻率
R32
补偿密度
R42
声波时差
拟合优度 0.522 0.227 0.413 0.408
关联序 1 4 2 3
Table 2  线性回归相关系数结果
γ(x0,x1)
深度曲线
γ(x0,x2)
自然伽马
γ(x0,x3)
自然电位
γ(x0,x4)
声波时差
γ(x0,x5)
补偿密度
γ(x0,x6)
补偿中子
γ(x0,x7)
深电阻率
γ(x0,x8)
浅电阻率
关联度 0.173 0.368 0.097 0.524 0.517 0.229 0.384 -0.085
关联序 6 4 7 1 2 5 3 8
Table 3  本文斜率关联度计算结果
Fig.2  X井15号煤层预测气含量曲线
Fig.3  预测含气量与煤层测试含气量间的关系
样品号 测试含气量/(m3·t-1) 预测含气量/(m3·t-1) 绝对误差/(m3·t-1) 平均相对误差/%
26 15.34 16.68 1.34 8
27 19.33 17.29 2.04 10
28 17.98 16.98 1.00 5
29 17.76 18.21 0.45 2
30 16.56 17.14 0.58 3
31 13.10 14.58 1.48 11
32 19.57 18.49 1.08 5
33 13.76 15.48 1.72 12
34 12.69 13.81 1.12 8
35 10.29 10.60 0.31 3
36 12.54 12.75 0.21 1
37 13.54 13.58 0.04 0
38 17.17 16.55 0.62 3
39 13.24 12.81 0.43 3
40 16.24 16.20 0.04 0
平均值 0.83 4.9
Table 4  测试含气量与灰色预测含气量的关系
建模个数 不同深度点对应的气含量预测值/(m3·t-1) 绝对误差
/(m3·t-1)
平均相对误差
/%
1 238.67 m 1 240.82 m 1 347.34 m 1 348.54 m 1 349.44 m
6 16.48 19.35 23.53 22.18 20.39 2.67 14.9
8 14.75 17.63 21.63 20.04 19.2 1.77 10.0
10 13.35 15.37 21.25 18.07 20.40 1.56 8.7
12 15.95 16.94 21.93 19.70 21.2 1.64 8.9
14 16.98 17.42 21.90 20.04 21.3 1.62 8.8
16 15.84 15.92 21.34 18.72 19.41 0.96 5.3
18 16.78 16.38 21.44 18.66 18.87 0.87 4.5
20 17.03 16.65 21.54 19.06 19.53 0.84 4.6
气含量 17.26 16.20 19.35 17.76 19.57
Table 5  GM(0,7)模型基于不同样本数预测深层煤层气含量结果
Fig.4  样本数据数量与预测平均误差的关系
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