Experimental analysis of oil-water nuclear magnetic relaxation characteristics of complex conglomerate reservoir
-
摘要: 玛湖凹陷砾岩储层岩性、孔隙结构复杂，造成储层流体识别困难. 通过区块原油样品获取原油不同温度下的体弛豫特征，并利用两种不同体弛豫特征的模拟油样开展实验；在此基础上，选取了部分砾岩样品通过真空加压饱和水、高速离心和真空饱和模拟油的方法快速建立了饱和水、束缚水和饱和油状态，进行流体及模拟不同油水饱和状态的核磁实验分析. 实验结果表明：岩心饱和水核磁T2谱的分布范围主要受表面弛豫影响，受油相体弛豫性质的影响，不同黏度油样饱和水状态的核磁谱均与饱和水核磁谱存在差异，就本次实验的岩心样品，油样越稀，差异越明显；含油核磁谱分布形态受油相体弛豫、表面弛豫、孔隙结构和润湿性的综合影响. 结合油样的体弛豫特性，利用多组分高斯拟合对两种油样饱和的砾岩核磁T2谱进行了分析，尝试对油相体弛豫信号进行了定量评价.Abstract: The lithology and pore structure of conglomerate reservoir in the Mahu sag, China, are complex, which makes it difficult to identify reservoir fluids. In this study, the body relaxation characteristics of crude oil at different temperatures were obtained from block crude oil samples, and two simulated oil samples with different body relaxation characteristics were used to conduct experiments. The saturated water, bound water, and saturated oil states of some conglomerate samples were quickly established by vacuum compression saturation, high-speed centrifugation, and vacuum saturation simulation of oil, and the fluid and different simulated oil-water saturation states were analyzed by nuclear magnetic resonance (NMR) experiments. The experimental results showed that the distribution range of the water-saturated core NMR T2 spectrum is mainly affected by surface relaxation. The NMR spectra of saturated oil with different viscosity values are different from those of saturated water because of the relaxation property of the oil phase. The samples of this experiment indicated that the difference is more obvious when the oil is thinner. The distribution pattern of the oil-bearing NMR results was affected by the relaxation of the oil phase, surface relaxation, pore structure, and wettability. With consideration of the relaxation characteristics of the oil samples, the NMR T2 spectra of two oil-saturated conglomerates were analyzed by multi-component Gaussian fitting, and the relaxation signals of the oil phase were evaluated quantitatively.
-
Key words:
- well logging /
- NMR /
- low permeability conglomerate /
- T2 distribution spectrum
-
图 1 玛湖凹陷砾岩储层岩石薄片微观特征
Figure 1. Microscopic characteristics of thin sections of conglomerate reservoir in the Mahu sag
图 2 不同温度下三种原油样的体弛豫T2谱
Figure 2. Bulk relaxation T2 spectra of three crude samples at different temperatures
图 4 四块岩心不同状态下的核磁共振T2谱. 各图中的图例依次为：饱和水状态、离心状态、饱和油A状态、饱和油B状态、油A体弛豫、油B体弛豫
Figure 4. NMR T2 spectra of four cores in different states. From top to bottom, the legend in each chart is: Saturated water state, centrifugal state, saturated oil A state, saturated oil B state, bulk relaxation T2 spectra of oil A, bulk relaxation T2 spectra of oil B
表 1 岩心基本参数
Table 1. Basic parameters of the samples
序号 岩心编号 岩石定名 层位 孔隙度/% 孔隙体积/ml 气体渗透率/（10−3μm2） 1 15555 灰褐色含砾泥岩 T1b 6.5 1.630 8.29 2 15575 灰色砂质砾岩 T1b 19.7 3.801 291.00 3 15577 灰色砂质砾岩 T1b 13.5 2.633 124.00 4 15563 灰色砂质砾岩 T1b 11.2 2.315 20.60 
下载: 导出CSV
表 2 饱和水与饱和油状态下核磁T2谱对比
Table 2. Comparison of T2 NMR spectra of saturated water and saturated oil
岩样号 饱水谱几何均值/ms 饱油谱几何均值/ms 饱水谱特征 饱油谱特征 15555 8.94 11.92 三峰，右峰小，水峰：35.5 ms 三峰，右峰较大，油峰：354.8 ms 15563 11.03 38.62 双峰，水峰：39.8 ms 三峰，右峰出现分离，油峰：112 ms 15575 11.09 36.49 双峰，水峰：50.1 ms 四峰，右峰出现分离，油峰：794.1 ms 15577 14.47 62.47 双峰，水峰：63.1 ms 三峰，右峰出现分离，油峰：354.2 ms
下载: 导出CSV
表 3 饱和油状态下核磁T2谱连续分布拟合结果
Table 3. Results of continuous distribution fitting of NMR T2 spectra in the saturated oil state
岩样号 $\phi_{总} $/% $\phi_{1} $/% $\phi_{2} $/% $\phi_{3} $/% $\phi_{4} $/% 含油谱特征峰值/ms 饱水谱特征峰值/ms 15563 10.2 2.5 0 6.0 1.7 0.679，89.83，965.6 0.875，44.176 15575 14.9 4.9 1.2 3.8 5.0 0.704，6.613，145.022，910.223 1.160，58.647 15577 11.7 2.7 1.3 5.3 2.4 0.841，13.903，221.008，1097.487 1.471，62.974 注： $\phi_{总} $为核磁计算总孔隙度（%） ； $\phi_{1} $~ $\phi_{4} $分别对应4种油谱组分区间孔隙度（%）
下载: 导出CSV
-
[1] 陈洪斌. 2003. 测井识别储层流体性质的方法研究及应用[J]. 天然气勘探与开发, 26（3）: 36-42Chen H B. 2003. Study and application of well logging to identify reservoir fluid properties[J]. Natural Gas Exploration and Development, 26(3): 36-42 (in Chinese). [2] 葛新民, 曾炳丁, 李楠, 等. 2021. 核磁共振饱和度指数及其在储层流体饱和度计算中的应用[J]. 中国石油大学学报（自然科学版）, 45（5）: 64-72Ge X M, Zeng B D, Li N, et al. 2021. NMR saturation index and its application to fluid saturation estimation[J]. Journal of China University of Petroleum (Edition of Natural Science), 45(5): 64-72(in Chinese). [3] doi: 10.19762/j.cnki. dizhixuebao.2022223.Li D, Hu K L, Zhang Y, et al. 2022. Logging evaluation method for reservoir fluid properties of Chang 8 oil layer to the west of Tianhuan depression-Pengyang region of Ordos basin[J]. Acta Geologica Sinica, 96(6): 2230-2239 (in Chinese). doi: 10.19762/j.cnki.dizhixuebao.2022223 [4] 李鹏举, 张智鹏, 姜大鹏. 2011. 核磁共振测井流体识别方法综述[J]. 测井技术, 35（5）: 396-401 doi: 10.16489/j.issn.1004-1338.2011.05.005Li P J, Zhang Z P, Jiang D P. 2011. Review on fluid identification methods with NMR logging[J]. Well Logging Technology, 35(5): 396-401(in Chinese). doi: 10.16489/j.issn.1004-1338.2011.05.005. [5] 李舟波. 2004. 利用测井方法识别复杂油气储层的流体性质[J]. 石油与天然气地质, 25（4）: 356-362Li Z B. 2004. Problem and advance on the fluid typing for complex reservoirs using well log[J]. Oil and Gas Geology, 25(4): 356-362 (in Chinese). [6] 刘国强, 龚仁彬, 石玉江, 等. 2022. 油气层测井知识图谱构建及其智能识别方法[J]. 石油勘探与开发, 49（3）: 502-512 doi: 10.1016/S1876-3804(22)60042-9Liu G Q, Gong R B, Shi Y J, et al. 2022. Construction of well logging knowledge graph and intelligent identification method of hydrocarbon-bearing formation[J]. Petroleum Exploration and Development, 49(3): 502-512 (in Chinese). doi: 10.1016/S1876-3804(22)60042-9 [7] 罗兴平, 苏东旭, 王振林, 等. 2017. 核磁共振测井在低阻油层评价中的应用——以准噶尔盆地阜东斜坡头屯河组为例[J]. 新疆石油地质, 38（4）: 470-476Luo X P, Su D X, Wang Z L, et al. 2017. Application of NMR logging in low-resistivity reservoir evaluation: A case study of Toutunhe Formation on the eastern Fukang Slope, Junggar Basin[J]. Xinjiang Petroleum Geology, 38(4): 470-476 (in Chinese). [8] 王龙, 何宗斌, 朱雷. 2017. 核磁共振测井油层识别方法综述[J]. 能源与环保, 39（5）: 198-201 doi: 10.19389/j.cnki.1003-0506.2017.05.041Wang L, He Z B, Zhu L. 2017. Review on recognition methods of oil reservior with NMR logging[J]. China Energy and Environmental Protection, 39(5): 198-201(in Chinese). doi: 10.19389/j.cnki.1003-0506.2017.05.041. [9] 肖立志. 2007. 核磁共振测井原理与应用[M]. 北京: 石油工业出版社.Xiao L Z. 2007. Principle and Application of NMR Logging[M]. Beijing: Press of Petroleum Industry (in Chinese). [10] 谢然红, 肖立志. 2007. 储层流体及其在岩石孔隙中的核磁共振弛豫温度特性[J]. 地质学报, 81（2）: 284-288Xie R H, Xiao L Z. 2007. Temperature effect on NMR relaxation time for bulk fluids and fluids in rocks[J]. Acta Geologica Sinica, 81(2): 284-288 (in Chinese). [11] 张妮, 王伟, 王振林, 等. 2018. 利用核磁共振测井判定低渗透砾岩储集层流体性质——以玛湖凹陷下三叠统百口泉组为例[J]. 新疆石油地质, 39（1）: 109-113Zhang N, Wang W, Wang Z L, et al. 2018. Using NMR logging to determine fluid properties in low-permeability conglomerate reservoirs: A case study from the lower Triassic Baikouquan Formation in Mahu sag[J]. Xinjiang Petroleum Geology, 39(1): 109-113 (in Chinese). [12] 张少华, 谭茂金, 李高仁, 等. 2021. 复杂油水层核磁共振测井时间域分析改进方法[J]. 石油物探, 60（4）: 686-692Zhang S H, Tan M J, Li G R, et al. 2021. Improved time domain analysis method for NMR logging in complex oil-water reservoirs[J]. Geophysical Prospecting for Petroleum, 60(4): 686-692 (in Chinese). [13] 赵军龙, 刘玲, 李新胜, 等. 2009. 低渗特低渗砂岩储层流体识别技术研究综述和展望[J]. 地球物理学进展, 24（4）: 1446-1453Zhao J L, Liu L, Li X S, et al. 2009. Review and forecast of technique research on fluid identification of low and particularly low permeability sandstone reservoir[J]. Progress in Geophysics, 24(4): 1446-1453 (in Chinese). -
点击查看大图
计量
- 文章访问数: 126
- HTML全文浏览量: 80
- PDF下载量: 16
- 被引次数: 0
