一种基于变骨架参数的孔隙度预测新方法

doi:10.11743/ogg20220319

Abstract

Abstract:

Porosity prediction from logging data is a common practice of quantitative evaluation of hydrocarbon reservoirs. The accuracy of reservoir porosity calculation directly affects the reliability of subsequent reservoir evaluation. For evaluating reservoirs with complex mineral components， the main challenge is how to quickly and accurately predict the porosity with logging data. One such example is the tuffaceous sandstone reservoir developed in L Formation of A Sag. Its complex and changeable sandy composition and late diagenesis result in logging responses varying significantly from one layer to another， making the applicability of the single-porosity model built based core-calibrated logging data and multivariate statistical method to porosity calculation of these layers rather questionable. To tackle the issue， this study proposes a porosity prediction method based on variable matrix parameters by using a volume physical model and through principal component analysis. Porosity prediction by using the method with actual logging data fits well with core analysis results and the workflow for separate-layer porosity interpretation is simplified. The method may serve as a certain reference for the prediction of porosity of rocks with complex components.

Key words: principal component analysis, variable matrix parameter, complex rock component, tuffaceous sandstone, porosity prediction, reservoir evaluation

CLC Number:

TE122.2

Bo Shen, Gang Wang, Haitang Fan, Jinfeng Zhang, Yanpu Li. A new method for porosity prediction based on variable matrix parameters[J]. Oil & Gas Geology, 2022, 43(3): 711-716.

Figures/Tables 9

Fig.1

Fig.2

Table 1

Table 2

Fig.3

Fig.4

Fig.5

Fig.6

Table 3

References 27

1	李军，路菁，李争，等.页岩气储层"四孔隙"模型建立及测井定量表征方法［J］.石油与天然气地质，2014，35（2）：266-271.
	Li Jun， Lu Jing， Li Zheng， et al. ‘Four‑pore’ modeling and its quantitative logging description of shale gas reservoir［J］.Oil & Gas Geology，2014，35（2）：266-271.
2	李军，武清钊，路菁，等.页岩气储层总孔隙度与有效孔隙度测量及测井评价-以四川盆地龙马溪组页岩气储层为例［J］.石油与天然气地质， 2017，38（3）：602-609.
	Li Jun， Wu Qingzhao， Lu Jing， et al. Measurement and logging evaluation of total porosity and effective porosity of shale gas reservoirs： A case from the Silurian Longmaxi Formation shale in the Sichuan Basin［J］.Oil & Gas Geology， 2017，38（3）：602-609.
3	李军，金武军，王亮，等.利用核磁共振技术确定有机孔与无机孔孔径分布-以涪陵地区志留系页岩气储层为例［J］.石油与天然气地质，2016，37（1）：129-134.
	Li Jun， Jin Wujun， Wang Liang， et al. Quantitative evaluation of organic and inorganic pore size distribution by NMR‑Taking the Fuling Silurian shale gas as an example［J］.Oil & Gas Geology，2016，37（1）：129-134.
4	Sondergeld C H， Newsham K E， Comisky J T， et al. Petrophysical considerations in evaluation and producing shale gas resources［J］.SPE， 2010，131768：1-29.
5	曲岩涛，戴志坚，李桂梅，等. 岩心分析方法［S］.北京：石油工业出版社，2007：71-84.
	Qu Yantao， Dai Zhijian， Li Guimei， et al. Practices for core ana⁃lysis，［S］. Beijing： Petroleum Industry Press，2007： 71-84.
6	张岩，黄晓莹.岩心孔隙结构的空间分辨核磁表征［J］. 中国石油大学学报（自然科学版），2021，45（4）： 49-58.
	Zhang Yan， Huang Xiaoying. Spatially resolved NMR for rock core analysis ［J］.Journal of China University of Petroleum（Edition of Natural Science）， 2021， 45（4）： 49-58.
7	郭龙龙，陈洪德，黄晓波，等.富长石粗碎屑砂岩孔隙演化定量分析——以渤海湾盆地辽东凸起北段沙河街组二段为例［J］.石油与天然气地质，2020，41（4）：874-883.
	Guo Longlong， Cheng Hongde， Huang Xiaobo，et al.Quantitative analysis on pore evolution in feldspar‑rich coarse clastic sandstone：A case study of Es2 in the north part of Liaodong Uplift［J］.Oil & Gas Geology，2020，41（4）：874-883.
8	余秋均．多井测井资料的标准化处理方法［J］．石油地质与工程，2020，34（6）：118-122．
	Yu Qiujun. Research on standardized processing method of multi⁃well logging data［J］. Petroleum Geology & Engineering， 2020， 34（6）： 118-122．
9	魏赫鑫，赖枫鹏，蒋志宇，等.延长致密气储层微观孔隙结构及流体分布特征［J］.断块油气田，2020，27（2）：182-187.
	Wei Hexin， Lai Fengpeng， Jiang Zhiyu，et al.Micropore structure and fluid distribution characteristics of Yanchang tight gas reservoir［J］.Fault‑Block Oil and Gas Field，2020，27（2）：182-187.
10	李兆亮，柳金城，王琳，等.基于神经网络的复杂储层流体分级识别［J］.断块油气田，2020，27（4）：498-500.
	Li Zhaoliang， Liu Jincheng， Wang Lin，et al.Fluid hierarchical identification of complex reservoir based on neural network method［J］.Fault‑Block Oil and Gas Field，2020，27（4）：498-500.
11	魏峰，李跃林.12基于测录井资料的复杂流体性质识别技术［J］.断块油气田，2020，27（6）：760-765.
	Wei Feng， Li Yuelin.Identification technology of complex fluid properties based onlogging and mud logging data［J］.Fault‑Block Oil and Gas Field，2020，27（6）：760-765.
12	白龙辉，柳波，迟亚奥等.二维核磁共振技术表征页岩所含流体特征的应用［J］.石油与天然气地质，2021，42（6）：1389-1400.
	Bai Longhui， Liu Bo， Chi Yaao，et al.2D NMR studies of fluids in organic⁃rich shale from the Qingshankou Formation， Songliao Basin［J］. Oil & Gas Geology，2021，42（6）：1389-1400.
13	钟红利，张凤奇，赵振宇，等. 致密砂岩储层微观孔喉分布特征及对可动流体的控制作用［J］. 石油实验地质， 2021， 43（1）： 77-85.
	Zhong Hongli， Zhang Fengqi， Zhao Zhenyu， et al. Micro‑scale pore‑throat distributions in tight sandstone reservoirs and its constrain to movable fluid［J］.Petroleum Geology & Experiment，2021， 43（1）： 77-85.
14	Wise B M， Ricker N L， Velt D J， et al. A theoretical basis for the use of principal components models for monitoring multivariate processes ［S］. Process Control and Quality， 1990， 1（1）： 41-51.
15	周游，张广智，高刚，等.核主成分分析法在测井浊积岩岩性识别中的应用［J］.石油地球物理勘探，2019，54（3）：667-675，490.
	Zhou You， Zhang Guangzhi， Gao Gang， et al. Application of kernel principal component analysis in well logging turbidite lithology identification［J］.Oil Geophysical Prospecting. 2019， 54（3）：667-675，490
16	Draper B A， Baek K， Bartlett M S， et al. Recognizing faces with PCA and ICA［J］.Computer Vision Image Understanding，2003，91（1）：115-137.
17	Chattopadhyay A K， Mondal S， Chattopadhyay T. Independent component analysis for the objective classification of globular clusters of the galaxy NGC 5128［J］.Computational Statistics & Data Analysis，2013，57（1）：17-32.
18	刘爱疆，左烈，李景景，等.主成分分析法在碳酸盐岩岩性识别中的应用-以YH地区寒武系碳酸盐岩储层为例［J］.石油与天然气地质，2013，34（2）：192-196.
	Liu Aijiang， Zuo Lie， Li Jingjing， Application of principal component analysis in carbonate lithology identification： A case study of the Cambrian carbonate reservoir in YH field［J］. Oil & Gas Geo⁃logy， 2013，34（2）：192-196.
19	Sharma P， Jyoti B P. Water quality assessment using water quality index and principal component analysis： A case study of historically important lakes of Guwahati City， North‑East India［J］. Applied Ecology and Environmental Sciences， 2020， 8（5）：207-217.
20	胡红，曾恒英，梁海波，等.基于主成分分析和学习矢量化的神经网络岩性识别方法［J］.测井技术，2015，39（5）：586-590.
	Hu Hong， Zeng Hengying， Liang Haibo， et al. Lithology identification based on principal component analysis and learning vector quantization neural network［J］. Well Logging Technology， 2015， 39（5）：586-590.
21	祝鹏，林承焰，吴鹏，等.基于主成分分析法的成岩相测井定量识别——以五号桩油田桩62-66块沙三下Ⅰ油组储层为例［J］.地球物理学进展，2015，30（5）：2360-2365.
	Zhu Peng， Lin Chengyan， Wu Peng， et al. Logging quantitative identification of diagenetic facies by using principal component analysis： A case of Es3x1 in Zhuang 62-66 area， Wu Hao zhuang Oilfield［J］. Progress in Geophysics， 2015，30（5）：2360-2365.
22	葛新民，范卓颖，范宜仁，等.基于核主成分-小波能谱分析的复杂储层油水界面预测［J］.中南大学学报（自然科学版），2015，46（5）：1747-1753.
	Ge Xinmin， Fan Zhuoying， Fan Yiren， et al. Oil water contact prediction of complex reservoir using kernel principal component analysis and wavelet power spectrum analysis［J］. Journal of Central South University （Natural Science edition）， 2015， 46（5）：1747-1753.
23	陈修，徐守余，等. 基于支持向量机和主成分分析的辫状河储层夹层识别［J］. 中国石油大学学报（自然科学版），2021，45（4）： 22-31.
	Chen Xiu， Xu Shouyu， Li Shunming，et al.Identification of interlayers in braided river reservoir based on support vector machine and principal component analysis ［J］.Journal of China University of Petroleum（Edition of Natural Science）， 2021， 45（4）： 22-31.
24	张继伟.基于主成分分析的页岩油有利区评价——以仪陇—平昌地区大安寨段为例［J］.断块油气田，2021，（1）：28-32.
	Zhang Jiwei.Evaluation of favorable areas of shale oil based on principal component analysis： Taking Daanzhai member of Yilong⁃Pingchang area as an example［J］.Fault‑Block Oil and Gas Field，2021，（1）：28-32.
25	林海明.对主成分分析法运用中十个问题的解析［J］.统计与决策，2007， 8：16-18.
	Lin Haiming. Analysis of ten problems in the application of principal component analysis［J］. Statistics and Decision， 2007， 8：16-18.
26	斯伦贝谢公司. 测井解释常用岩石矿物手册［M］.吴庆岩，张爱军译.北京：石油工业出版社， 1998.
	Schlumberger. Well logging handbook of rocks and mineral［M］. Wu Qingyuan， Zhang Aijun， Trans. Beijing： Petroleum Industry Press， 1988.
27	谭廷栋.岩性和孔隙流体性质的弹性模量识别法［J］.天然气工业， 1986，6（1）：33-38.
	Tan Tingdong. Identifying lithological characteristics and the type of pore fluid in line of elastic moduli［J］. Natural Gas Industry， 1986，6（1）：33-38.

岩性	密度/（g·cm^-3）	中子测井值/（v·v^-1）	声波时差/（μs·ft^-1）
砂岩	2.65	-0.02	58
碳酸盐岩	2.68	0.05	59
火山岩	2.59	0.12	56

主成分	初始特征值			主成分矩阵
主成分	特征值	方差	累计方差	M	N	P
F₁	1.981	66.025	66.025	1.000	0.778	-0.613
F₂	1.019	33.961	99.985	-0.004	0.628	0.790

解释方法	孔隙度预测值/%	孔隙度分析值/%	平均绝对误差/%
声波时差	16.01	15.72	1.23
多元统计	14.98	15.72	1.59
变骨架参数	15.59	15.72	0.95

[1]	Wang Guoting, Jia Ailin, Yan Haijun, Guo Zhi, Meng Dewei, Cheng Lihua. Characteristics and recoverability evaluation on the potential reservoir in Sulige tight sandstone gas field [J]. Oil & Gas Geology, 2017, 38(5): 896-904.
[2]	Li Jun, Lu Jing, Li Zheng, Wu Qingzhao, Nan Zeyu. ‘Four-pore’modeling and its quantitative logging description of shale gas reservoir [J]. Oil & Gas Geology, 2014, 35(2): 266-271.
[3]	Zheng Lei, Jin Zhijun, Zhang Shaonan. Features and evaluation of the Palaeozoic tight sandstone reservoirs of the Rub Al Khali Basin in the Middle East [J]. Oil & Gas Geology, 2013, 34(4): 475-482.
[4]	Zhu Wei, Gu Shaoqiu, Cao Zijian, Zhang Guoyi. Fuzzy mathematics-based reservoir evaluation in the southeastern Pre-Caspian Basin [J]. Oil & Gas Geology, 2013, 34(3): 357-362.
[5]	Liu Aijiang, Zuo Lie, Li Jingjing, Li Rui, Zhang Wei. Application of principal component analysis in carbonate lithology identification: a case study of the Cambrian carbonate reservoir in YH field [J]. Oil & Gas Geology, 2013, 34(2): 192-196.
[6]	Xiao Dianshi, Lu Shuangfang, Chen Haifeng, Lu Yanping, Li Zhandong, Zhou Juan. Logging data normalization based on frequency spectral decomposition [J]. Oil & Gas Geology, 2013, 34(1): 129-136.
[7]	Hou Ruiyun, Liu Zhongqun. Resevoir evaluation and development strategies of Daniudi tight sand gas field in the Ordos Basin [J]. Oil & Gas Geology, 2012, 33(1): 118-128.
[8]	Liu Bin, Fu Xiaoyue, Liu Guoping, Feng Qiong, You Yuchun. Prediction and evaluation of reservoirs in Heba area, the Northeastern Sichuan Basin [J]. Oil & Gas Geology, 2008, 29(5): 690-696,702.
[9]	Fan Zhenjun, Liu Jianhua, Zhang Weifeng. Log interpretation and evaluation of the Ordovician carbonate rock reservoirs in Tahe oilfield [J]. Oil & Gas Geology, 2008, 29(1): 61-65.
[10]	Zhang Dawei, Chen Fajing, Cheng Gang. The microscopic features of pore structure in Gaotaizi oil layer of Daqingzijing area [J]. Oil & Gas Geology, 2006, 27(5): 668-674.
[11]	Wang Zhigao, Jin Yanxin. Dynamic division of flow units:taking Xianhezhuang oilfield as an example [J]. Oil & Gas Geology, 2005, 26(1): 125-129.
[12]	Yang Shaochun, Xin Quanlin. LOG RESERVOIR EVALUATION IN FAULT BLOCK POOLS [J]. Oil & Gas Geology, 1999, 20(1): 85-89.

A new method for porosity prediction based on variable matrix parameters

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 27

Related Articles 12

Recommended Articles

Metrics