在核磁共振横向弛豫时间（T2）谱特征分析的基础上,利用信号分析方法分解T2谱,提出了利用T2谱分解法识别流体性质的新方法。由于T2谱在横向弛豫时间轴上满足对数正态分布的特征,采用高斯函数对T2谱进行拟合,可将T2谱分解为2~5个独立的分量谱。通过分析原油和地层水自由弛豫响应特征以及岩心油水互驱的动态响应特征,明确了各分量谱的岩石物理意义。T2谱可以分解为黏土束缚水分量谱、毛细管束缚流体分量谱、小孔隙流体分量谱和大孔隙流体分量谱。依据目标区原油性质,确定含油储集层T2分量谱峰在T2时间轴上的分布范围为165~500 ms。据此可准确识别流体性质,对于低孔隙度、低渗透率储集层中复杂油水层的识别,具有较强的适应性。图9表2参13
钟吉彬
,
阎荣辉
,
张海涛
,
冯伊涵
,
李楠
,
刘行军
. 核磁共振横向弛豫时间谱分解法识别流体性质[J]. 石油勘探与开发, 2020
, 47(4)
: 691
-702
.
DOI: 10.11698/PED.2020.04.05
Based on analysis of NMR T2 spectral characteristics, a new method for identifying fluid properties by decomposing T2 spectrum through signal analysis has been proposed. Because T2 spectrum satisfies lognormal distribution on transverse relaxation time axis, the T2 spectrum can be decomposed into 2 to 5 independent component spectra by fitting the T2 spectrum with Gauss functions. By analyzing the free relaxation response characteristics of crude oil and formation water, the dynamic response characteristics of the core mutual drive between oil and water, the petrophysical significance of each component spectrum is clarified. T2 spectrum can be decomposed into clay bound water component spectrum, capillary bound fluid component spectrum, micropores fluid component spectrum and macropores fluid component spectrum. According to the nature of crude oil in the target area, the distribution range of T2 component spectral peaks of oil-bearing reservoir is 165-500 ms on T2 time axis. This range can be used to accurately identify fluid properties. This method has high adaptability in identifying complex oil and water layers in low porosity and permeability reservoirs.
[1] 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: 科学出版社, 1998.
XIAO Lizhi.Nuclear magnetic resonance imaging logging and rock nuclear magnetic resonance and its application[M]. Beijing: Science Press, 1998.
[2] 刘堂宴, 章海宁, 石玉江. 储层孔隙结构评价技术及应用: 球管模型理论与方法实现[M]. 北京: 石油工业出版社, 2017.
LIU Tangyan, ZHANG Haining, SHI Yujiang.Reservoir pore structure evaluation technology and application: Theory and method realization of spherical tube model[M]. Beijing: Petroleum Industry Press, 2017.
[3] 冯程, 石玉江, 郝建飞, 等. 低渗透复杂润湿性储集层核磁共振特征[J]. 石油勘探与开发, 2017, 44(2): 252-257.
FENG Cheng, SHI Yujiang, HAO Jianfei, et al.Nuclear magnetic resonance features of low-permeability reservoirs with complex wettability[J]. Petroleum Exploration and Development, 2017, 44(2): 252-257.
[4] 肖立志. 我国核磁共振测井应用中的若干重要问题[J]. 测井技术, 2007, 31(5): 401-407.
XIAO Lizhi.Some important issues for NMR logging applications in China[J]. Well Logging Technology, 2007, 31(5): 401-407.
[5] JIANG T M, JAIN V, BELOTSERKOVSKAYA A, et al.Evaluating producible hydrocarbons and reservoit quality in organic shale reservoirs using Nuclear Magnetic Resonance (NMR) factor analysis[R]. SPE 175893-MS, 2015.
[6] JAIN V, MINH C C, HEATON N, et al.Characterization of underlying pore and fluid structure using factor analysis on NMR data[R]. New Orleans: SPWLA 54th Annual Logging Symposium, 2013.
[7] 胡法龙, 周灿灿, 李潮流, 等. 核磁共振测井构建水谱法流体识别技术[J]. 石油勘探与开发, 2016, 43(2): 244-252.
HU Falong, ZHOU Cancan, LI Chaoliu, et al.Water spectrum method of NMR logging for identifying fluids[J]. Petroleum Exploration and Development, 2016, 43(2): 244-252.
[8] 贾俊平, 何晓群, 金勇进. 统计学[M]. 北京: 中国人民大学出版社, 2014.
JIA Junping, HE Xiaoqun, JIN Yongjin.Statistics[M]. Beijing: Renmin University of China Press, 2014.
[9] DU P, KIBBE W A, LIN S M.Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching[J]. Bioinformatics, 2006, 22(17): 2059-2065.
[10] 肖立志, 谢然红. 核磁共振在石油测井与地层油气评价中的应用[J]. 中国工程科学, 2003, 5(9): 87-94.
XIAO Lizhi, XIE Ranhong.Applications of NMR to oil well logging and formation evaluation[J]. Engineering Science, 2003, 5(9): 87-94.
[11] 王忠东, 汪浩, 李能根, 等. 核磁共振岩心基础实验分析[J]. 测井技术, 2001, 25(3): 170-174.
WANG Zhongdong, WANG Hao, LI Nenggen, et al.Analysis of core NMR data from laboratory measurements[J]. Well Logging Technology, 2001, 25(3): 170-174.
[12] 何宗斌, 倪静. 稠油油样核磁共振与粘度实验测量结果及研究[J]. 石油天然气学报, 2006, 28(5): 82-84.
HE Zongbin, NI Jing.Experimental results and study on nuclear magnetic resonance and viscosity of viscous oil samples[J]. Journal of Oil and Gas Technology, 2006, 28(5): 82-84.
[13] 肖立志, 谢然红, 廖广志. 中国复杂油气藏核磁共振测井理论与方法[M]. 北京: 科学出版社, 2012.
XIAO Lizhi, XIE Ranhong, LIAO Guangzhi.Theory and method of NMR logging for chinese complex oil and gas reservoirs[M]. Beijing: Science Press, 2012.
