致密气核磁共振测井观测模式及气水弛豫分析——以四川盆地为例
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摘要
四川盆地致密储层岩性、储集类型复杂,隐蔽性与非均质性强,气水关系难以准确评价。核磁共振测井方法可以解决常规测井的多解性问题,实现对储层流体性质的准确评价。为此,针对该盆地致密储层特征,开展核磁共振测井观测模式对比分析,依据不同观测模式的核磁共振测井信息,结合试验井测试资料,优选了核磁共振测井观测模式;进而以岩石弛豫特征和气水弛豫特征为理论基础,对已测试的致密储层T_2气水弛豫特征进行分析,剖析致密储层受孔隙度、孔径尺寸、流体性质等因素影响的T_2横向弛豫分布谱。研究结果认为:(1)D9TWE3为四川盆地致密储层最合适的核磁共振测井观测模式;(2)储层致密化是致密碎屑岩储层气水弛豫分布特征的主要影响因素,致密砂岩储层气水弛豫分布特征为天然气的T_2弛豫时间比水的T_2弛豫时间长;(3)缝洞型碳酸盐岩储层气水弛豫分布特征为气层的T_2分布谱右峰靠前、水层的T_2分布谱右峰靠后。结论认为,所形成的四川盆地致密砂岩储层和碳酸盐岩储层的T_2气水弛豫判别标准,能有效甄别出孔隙度介于4%~10%的致密储层流体性质,为四川盆地致密储层的气水识别、天然气储量计算、产能建设提供了技术支撑。
With complex lithology and reservoir types, as well as high concealment and heterogeneity, tight reservoirs in the Sichuan Basin involve significant uncertainties in gas–water relationship. Since NMR logging can effectively solve problems related to the multiple results of conventional logging operations, it can be deployed for accurate assessment of the properties of formation fluids. Accordingly, different NMR logging activation sets were assessed in accordance with the specific features of tight reservoirs in the basin. With consideration to NMR logging data obtained under different activation sets and testing data of wells, the optimal NMR logging activation set was identified. Moreover, with relaxation characteristics of rocks, gas and water as theoretical foundations, the T_2 gas and water relaxation characteristics were reviewed to highlight the impacts of porosity, pore sizes, fluid properties and other factors of tight reservoirs on T_2 horizontal relaxation distribution. According to the research results, D9TWE3 can be seen as the most suitable NMR logging activation set for tight reservoirs in the Sichuan Basin; reservoir tightness is the key influence factor for the distribution of gas/water relaxation in tight clastic reservoirs; generally, in tight sandstone reservoirs, natural gas shows a longer T_2 relaxation time than water; in fracture-vug type carbonate reservoirs, the right peak of T_2 distribution spectrum of gas layers is frontal, while the right peak in T_2 distribution spectrum of water layers is backward. In conclusion, the standards for gas/water relaxation in tight sandstone and carbonate reservoirs in the Sichuan Basin can help effectively determine the physical properties of fluids in tight reservoirs with porosity of 4–10%. Such standards provide reliably technical supports for gas/water identification, reserves estimation and productivity construction in tight reservoirs of the Sichuan Basin.
With complex lithology and reservoir types, as well as high concealment and heterogeneity, tight reservoirs in the Sichuan Basin involve significant uncertainties in gas–water relationship. Since NMR logging can effectively solve problems related to the multiple results of conventional logging operations, it can be deployed for accurate assessment of the properties of formation fluids. Accordingly, different NMR logging activation sets were assessed in accordance with the specific features of tight reservoirs in the basin. With consideration to NMR logging data obtained under different activation sets and testing data of wells, the optimal NMR logging activation set was identified. Moreover, with relaxation characteristics of rocks, gas and water as theoretical foundations, the T_2 gas and water relaxation characteristics were reviewed to highlight the impacts of porosity, pore sizes, fluid properties and other factors of tight reservoirs on T_2 horizontal relaxation distribution. According to the research results, D9TWE3 can be seen as the most suitable NMR logging activation set for tight reservoirs in the Sichuan Basin; reservoir tightness is the key influence factor for the distribution of gas/water relaxation in tight clastic reservoirs; generally, in tight sandstone reservoirs, natural gas shows a longer T_2 relaxation time than water; in fracture-vug type carbonate reservoirs, the right peak of T_2 distribution spectrum of gas layers is frontal, while the right peak in T_2 distribution spectrum of water layers is backward. In conclusion, the standards for gas/water relaxation in tight sandstone and carbonate reservoirs in the Sichuan Basin can help effectively determine the physical properties of fluids in tight reservoirs with porosity of 4–10%. Such standards provide reliably technical supports for gas/water identification, reserves estimation and productivity construction in tight reservoirs of the Sichuan Basin.
引文
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[13]张晋言,刘海河,刘伟.核磁共振测井在深层砂砾岩孔隙结构及有效性评价中的应用[J].测井技术,2012,36(3):256-260.Zhang Jinyan,Liu Haihe&Liu Wei.Application of NMR data to evaluation of deep glutenite pore structure and reservoir validity[J].Well Logging Technology,2012,36(3):256-260.
[14]Akkurt R,Guillory AJ,Vinegar HJ&Tutunjian PN.NMR logging of natural gas reservoirs[J].The Log Analyst,1996,37(6):33-42.
[15]Sun B&Dunn KJ.Core analysis with two dimensional NMR[C]//Proceedings of the 2002 International Symposium of the Society of Core Analysts.
[16]孙鲁平,首皓,赵晓龙,李平.基于微电阻率扫描成像测井的沉积微相识别[J].测井技术,2009,33(4):379-383.Sun Luping,Shou Hao,Zhao Xiaolong&Li Ping.Sedimentary facies identification based on FMI imaging logging data[J].Well Logging Technology,2009,33(4):379-383.
[2]Labani MM,Kadkhodaie-Ilkhchi A&Salahshoor K.Estimation of NMR log parameters from conventional well log data using a committee machine with intelligent systems:A case study from the Iranian part of the South Pars Gas Field,Persian Gulf Basin[J].Journal of Petroleum Science and Engineering,2010,72(1/2):175-185.
[3]肖立志,柴细元,孙宝喜,陆大卫,付有升,高秋涛.核磁共振测井资料解释与应用导论[M].北京:石油工业出版社,2001.Xiao Lizhi,Chai Xiyuan,Sun Baoxi,Lu Dawei,Fu Yousheng&Gao Qiutao.NMR logging interpretation and China case studies[M].Beijing:Petroleum Industry Press,2001.
[4]肖立志.核磁共振成像测井与岩石核磁共振及其应用[M].北京:北京科学出版社,1998.Xiao Lizhi.NMRimagingloggingprin ciples and applications[M].Beijing:Science Press,1998.
[5]张筠.川西坳陷裂缝性储层的裂缝测井评价技术[J].天然气工业,2003,23(增刊1):43-45.Zhang Yun.Fracture log evaluation techniques of the fractured reservoirs in West Sichuan Depression[J].Natural Gas Industry,2003,23(S1):43-45.
[6]吴见萌,葛祥,张筠,董震.核磁共振测井在川西低孔隙度低渗透率储层中的应用[J].测井技术,2010,34(2):159-163.Wu Jianmeng,Ge Xiang,Zhang Yun&Dong Zhen.The application of the NMR logging to low porosity and permeability reservoir in west Sichuan[J].Well Logging Technology,2010,34(2):159-163.
[7]吴见萌,张筠,葛祥.利用核磁共振测井资料评价致密储层可动水饱和度[J].测井技术,2011,35(6):559-563.Wu Jianmeng,Zhang Yun&Ge Xiang.NMR logging evaluation of mobile water saturation in tight reservoir[J].Well Logging Technology,2011,35(6):559-563.
[8]李宁,肖承文,伍丽红,石玉江,武宏亮,冯庆付,等.复杂碳酸盐岩储层测井评价:中国的创新与发展[J].测井技术,2014,38(1):1-10.Li Ning,Xiao Chengwen,Wu Lihong,Shi Yujiang,Wu Hongliang,Feng Qingfu,et al.The innovation and development of log evaluation for complex carbonate reservoir in China[J].Well Logging Technology,2014,38(1):1-10.
[9]颜其彬,赵辉,司马立强,施振飞.碳酸盐岩核磁共振实验与认识[J].天然气工业,2010,30(1):36-38.Yan Qibin,Zhao Hui,Sima Liqiang&Shi Zhenfei.A study of NMR experiments of carbonates[J].Natural Gas Industry,2010,30(1):36-38.
[10]谢然红,肖立志,邓克俊,廖广志,刘天定.二维核磁共振测井[J].测井技术,2005,29(5):430-434.Xie Ranhong,Xiao Lizhi,Deng Kejun,Liao Guangzhi&Liu Tianding.Two-dimensional NMR logging[J].Well Logging Technology,2005,29(5):430-434.
[11]谢然红,肖立志.(T2,D)二维核磁共振测井识别储层流体的方法[J].地球物理学报,2009,52(9):2410-2418.Xie Ranhong&Xiao Lizhi.The(T2,D)NMR logging method for fluids characterization[J].Chinese Journal of Geophysics,2009,52(9):2410-2418.
[12]李鹏举,张智鹏,姜大鹏.核磁共振测井流体识别方法综述[J].测井技术,2011,35(5):396-401.Li Pengju,Zhang Zhipeng&Jiang Dapeng.Review on fluid identification methods with NMR logging[J].Well Logging Technology,2011,35(5):396-401.
[13]张晋言,刘海河,刘伟.核磁共振测井在深层砂砾岩孔隙结构及有效性评价中的应用[J].测井技术,2012,36(3):256-260.Zhang Jinyan,Liu Haihe&Liu Wei.Application of NMR data to evaluation of deep glutenite pore structure and reservoir validity[J].Well Logging Technology,2012,36(3):256-260.
[14]Akkurt R,Guillory AJ,Vinegar HJ&Tutunjian PN.NMR logging of natural gas reservoirs[J].The Log Analyst,1996,37(6):33-42.
[15]Sun B&Dunn KJ.Core analysis with two dimensional NMR[C]//Proceedings of the 2002 International Symposium of the Society of Core Analysts.
[16]孙鲁平,首皓,赵晓龙,李平.基于微电阻率扫描成像测井的沉积微相识别[J].测井技术,2009,33(4):379-383.Sun Luping,Shou Hao,Zhao Xiaolong&Li Ping.Sedimentary facies identification based on FMI imaging logging data[J].Well Logging Technology,2009,33(4):379-383.