基于孔隙结构和核磁测井建立火山岩储层分类标准——以松南断陷查干花气田为例

doi:10.13809/j.cnki.cn32-1825/te.2024.02.007

Abstract

Abstract:

In the Chaganhua Gas Field within the Songnan Fault Depression, the Huoshiling Formation's volcanic reservoirs exhibit an average porosity of 4.5% and a permeability of 0.08×10^-3 μm, indicating a dense and highly heterogeneous nature. Due to this complexity, a comprehensive approach, testing a broad set of reservoirs, is required to establish effective classification criteria. This study used physical property data, high-pressure mercury injection, nuclear magnetic resonance, and other experiments to analyze the microstructure of volcanic reservoirs. Through multi parameter comparison, a microscopic classification standard was established. Nuclear magnetic logging served as a bridge between microscopic and macroscopic parameters, facilitating the creation of a comprehensive evaluation framework for classifying volcanic reservoirs. This framework encompasses microscopic structural features such as pore throat radius, displacement pressure, mercury saturation, alongside macroscopic parameters obtained from nuclear magnetic logging and other experiments, such as the T2 spectrum distribution, centrifugal saturation, porosity, permeability, saturation, acoustic time difference, lithology density, and resistivity. Reservoirs are categorized from high to low quality into classes A, B, and C based on this comprehensive set of criteria. This method has strong operability and provides a reliable basis for the testing plan of new drilling and the optimization of sweet spots in exploration and development of horizontal wells. The research methods and understanding have certain reference significance for the classification research of volcanic reservoirs.

Key words: Songnan Fault Depression, Chaganhua Gas Field, reservoir classification and evaluation, nuclear magnetic logging, microstructure

CLC Number:

TE377

WANG Min,CAO Yue,LI Wancai,ZHAO Wenqi,WANG Wenyong,SONG Yuying. Establishing classification standards for volcanic reservoirs based on pore structure and nuclear magnetic logging: A case study of Chaganhua Gas Field in Songnan Fault Depression[J].Petroleum Reservoir Evaluation and Development, 2024, 14(2): 216-223.

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References 21

[1]	徐永强, 何永宏, 卜广平, 等. 基于微观孔喉结构及渗流特征建立致密储层分类评价标准[J]. 石油实验地质, 2019, 3(41): 451-460.
	XU Yongqiang, HE Yonghong, BU Guangping, et al. Establishment of classification and evaluation criteria for tight reservoirs based on characteristics of microscopic pore throat structure and percolation[J]. Petroleum Geology & Experiment, 2019, 3(41): 451-460.
[2]	萧高健. 鄂尔多斯盆地红河油田长8段致密裂缝砂岩储层表征及“甜点油层”综合评价研究[D]. 北京: 中国地质大学, 2022.
	XIAO Gaojian. Characterization and comprehensive evaluation of ‘Sweet Spots’in the Chang 8 tight fractured sandstone reservoir in Honghe Oilfield,Ordos Basin, China[D]. Beijing: A Dissertation Submitted to China University of Geosciences, 2022.
[3]	胡芸冰. 鄂尔多斯盆地南部长7致密油储层评价及分类[D]. 西安: 西北大学, 2017.
	HU Yunbing. Research on tight oil reservoirs geological evaluation and classification of the Chang7 member in southern Ordos Basin[D]. Xi'an: Northwest University, 2017.
[4]	田夏荷. 鄂尔多斯盆地东部本溪组-山西组致密气储层分类评价研究[D]. 西安: 西北大学, 2016.
	TIAN Xiahe. Research on tight gas reservoirs evaluation and classification of Benxi formation-Shanxi formation in eastern Ordos Basin[D]. Xi'an: Northwest University, 2016.
[5]	王跃祥, 谢冰, 赖强, 等. 基于核磁共振测井的致密气储层孔隙结构评价与分类[J]. 地球物理学进展, 2023, 38(2): 759-767.
	WANG Yuexiang, XIE Bing, LAI Qiang, et al. Evaluation of pore structure and classification in tight gas reservoir based on NMR logging[J]. Progress in Geophysics. 2023, 38(2): 759-767.
[6]	金国文, 王堂宇, 刘忠华, 等. 基于核磁共振测井的砂砾岩储层分类与产能预测方法[J]. 石油学报, 2022, 43(5): 648-657. doi: 10.7623/syxb202205006
	JIN Guowen, WANG Tangyu, LIU Zhonghua, et al. Classification and productivity prediction of glutenite reservoirs based on NMR logging[J]. Acta Petrolei Sinica, 2022, 43(5): 648-657. doi: 10.7623/syxb202205006
[7]	朱玉瑞. 东营凹陷沙河街组致密砂岩储层孔隙结构表征及分类评价[D]. 四川: 成都理工大学, 2021.
	ZHU Yurui. Pore structure characteristics and classification of tight sands from Shahejie Formation in Dongying Sag[D]. Sichuan: Chengdu University of Technology, 2021.
[8]	卫弘媛, 谢然红, 王跃祥, 等. 四川盆地沙溪庙组致密砂岩储层孔隙结构与分类[J]. 东北石油大学学报, 2023, 47(3): 34-43.
	WEI Hongyuan, XIE Ranhong, WANG Yuexiang, et al. Pore structure characterization and reservoir classification in tight sandstone reservoirs of the Shaximiao Formation in Sichuan Basin[J]. Journal of Northeast Petroleum University, 2023, 47(3): 34-43.
[9]	柴晓龙, 田冷, 孟艳, 等. 鄂尔多斯盆地致密储层微观孔隙结构特征与分类[J]. 天然气地球科学, 2023, 34(1): 33-59.
	CHAI Xiaolong, TIAN Leng, MENG Yan, et al. Study on characteristics and classification of micro-pore structure in tight reservoirs for Ordos Basin[J]. Natural Gas Geoscience, 2023, 34(1): 33-59.
[10]	周丽艳, 罗少成, 林伟川, 等. 基于孔隙结构综合评价指数的致密砂岩储层分类方法[J]. 测井技术, 2022, 46(6): 707-713.
	ZHOU Liyan, LUO Shaocheng, LIN Weichuan, et al. Classification method of tight sandstone reservoir based on comprehensive evaluation index of pore structure[J]. Well Logging Technology, 2022, 46(6): 707-713.
[11]	MANDELBROT B B. On the geometry of homogeneous turbulence, with stress on the fractal dimension of the iso-surfaces of scalars[J]. Journal of Fluid Mechanics, 2006, 72(3): 401-416. doi: 10.1017/S0022112075003047
[12]	WANG F Y, YANG K, YUN Z. Multifractal characteristics of shale and tight sandstone pore structures with nitrogen adsorption and nuclear magnetic resonance[J]. Petroleum Science, 2020, 17(5): 1209-1220. doi: 10.1007/s12182-020-00494-2
[13]	ZHU H H, ZHANG T S, ZHONG D K, et al. Binary pore structure characteristics of tight sandstone reservoirs[J]. Petroleum Exploration and Development, 2019, 46(6): 1297-1306. doi: 10.1016/S1876-3804(19)60283-1
[14]	LIU H L, YANG Y Y, WANG F Q, et al. Micro pore and throat characteristics and origin of tight sandstone reservoirs: A case study of the Triassic Chang 6 and Chang 8 members in Longdong area, Ordos Basin, NW China[J]. Petroleum Exploration and Development, 2018, 45(2): 239-250. doi: 10.1016/S1876-3804(18)30027-2
[15]	XU C C, TORRES-VERDIN C. Petrophysical rock classification in the Cotton Valley tight gas sandstone reservoir with a clus-tering pore-system orthogonality matrix ore system orthogonality for rock typing[J]. Interpretation, 2014, 2(1): 13-23.
[16]	LI C X, LIU M, GUO B C. Classification of tight sandstone reservoirs based on NMR logging[J]. Applied Geophysics. 2019, 16(4): 549-558. doi: 10.1007/s11770-019-0793-y
[17]	ZHOU D, ZHUANG X, ZUO H F. A novel three parameter Weibull distribution parameter estimation using chaos simulated annealing particle swarm optimization in civil aircraft risk assessment[J]. Arabian Journal for Science and Engineering, 2021, 46(9): 8311-8328. doi: 10.1007/s13369-021-05467-0
[18]	LIU M, XIE R H, LI J, et al. A new NMR-data-based method for predicting petrophysical properties of tight sandstone res-ervoirs[J]. Energy Geoscience, 2023, 4(2): 64-71.
[19]	XI KL, CAO Y C, HAILE B G, et al. How does the pore-throat size control the reservoir quality and oiliness of tight sand-stones? The case of the Lower Cretaceous Quantou Formation in the southern Songliao Basin, China[J]. Marine and Petroleum Geology, 2016, 76: 1-15. doi: 10.1016/j.marpetgeo.2016.05.001
[20]	NELSON P H. Pore-throat sizes in sandstones, tight sandstones, and shales[J]. AAPG Bulletin, 2009, 93(3): 329-340. doi: 10.1306/10240808059
[21]	ZHANG L C, LU S F, XIAO D S, et al. Pore structure characteristics of tight sandstones in the northern Songliao Basin, China[J]. Marine and Petroleum Geology, 2017, 88: 170-180. doi: 10.1016/j.marpetgeo.2017.08.005

孔隙度/%	渗透率/10^-3μm²	平均孔喉半径/μm
3.467	0.011	0.012
2.597	0.023	0.012
3.642	0.032	0.021
3.109	0.015	0.016
3.075	0.014	0.016
3.481	0.019	0.015
3.735	0.035	0.037
3.145	0.039	0.016
3.515	0.021	0.017
2.951	0.015	0.013
2.785	0.014	0.012
3.678	0.017	0.011
3.530	0.014	0.012
2.518	0.012	0.013
3.328	0.016	0.014
平均	0.020	0.015

	参数	A类	B类	C类
微观参数	均值系数	[0.3，0.4]	[0.2，0.3）	<0.2
	分选系数	[1.2，1.7]	（1.7，2.5]	>2.5
	歪度系数	整体大于0 以粗歪度为主	整体小于0 以细歪度为主	整体小于0 以细歪度为主
	最大孔喉半径/μm	[0.30，1.00]	[0.05，0.30）	<0.05
	最大汞饱和度/%	>80	（45，65]	[30，45]
	排驱压力/MPa	[0.6，2.7]	[2.7，13.8]	>13.8
	孔喉半径中值/μm	>0.050	[0.004，0.050]	<0.004
	汞饱和度中值压力/MPa	<60	[60，97）	[97，200]
	渗透率分布峰值/%	（50，60]	（38，50]	[25，38]
	孔隙分布峰值/%	（15，31]	（8，15]	[4，8]
	退汞饱和度/%	（30，50]	（20，30]	[10，20]
核磁参数	T₂谱	频谱较宽、谱峰靠后	谱峰中等	谱峰较窄
	T₂谱孔隙分布	64 ms后谱占比大于40%	32 ms后谱占比大于40%	16 ms后谱占比大于40%
	离心饱和度/%	>30	[10，30]	<10
常规测井参数	孔隙度/%	≥6	[4，6）	[2.9，4）
	渗透率/10^-3μm²	>0.1	（0.05~0.1]	（0.01~0.05]
	含气饱和度/%	>50	（40~50]	（30~40]
	黏土含量/%	≤20	>20	>20
	声波时差/（μs/m）	≥200	[191，200）	[184，191）
	岩性密度/（g/cm³）	≤2.55	（2.55，2.59]	（2.59，2.62]
	电阻率/（Ω·m）	≥183	≥274	≥315

Establishing classification standards for volcanic reservoirs based on pore structure and nuclear magnetic logging: A case study of Chaganhua Gas Field in Songnan Fault Depression

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