(T_2,T_1)二维核磁共振数值模拟与流体响应分析(英文)

Numerical simulation of(T_2,T_1) 2D NMR and fluid responses

一维核磁共振(1D NMR)测井技术在流体识别中具有一定的局限性。二维核磁共振(2D NMR)测井能更多参数包括多孔介质中纵向弛豫时间(T1)和横向弛豫时间(T_2)。根据梯度场下二维核磁共振弛豫机理,研究二维核磁共振回波串模拟与反演,并提出了基于阻尼最小二乘LSQR方法和改进的截断奇异值分解法的混合反演算法。在梯度场下,根据多等待时间,模拟给定流体模型的一系列回波串,并利用混合算法反演合成的回波串,反演结果与给定的流体模型匹配较好。并利用此反演算法,对气水模型,轻质油水和稠油水模型进行了不同回波间隔、不同等待时间组的数值模拟实验。最后,系统考察了不同观测参数对反演结果和流体识别效果的影响。此外,还系统研究了信噪比对多种流体模型反演结果的影响。数值模拟结果表明,混合算法与优化的观测参数非常适用于气水模型和油水模型。

One-dimensional nuclear magnetic resonance （1D NMR） logging technology is limited for fluid typing, while two-dimensional nuclear magnetic resonance （2D NMR） logging can provide more parameters including longitudinal relaxation time （71） and transverse relaxation time （T2） relative to fluid types in porous media. Based on the 2D NMR relaxation mechanism in a gradient magnetic field, echo train simulation and 2D NMR inversion are discussed in detail. For 2D NMR inversion, a hybrid inversion method is proposed based on the damping least squares method （LSQR） and an improved truncated singular value decomposition （TSVD） algorithm. A series of spin echoes are first simulated with multiple waiting times （Tws） in a gradient magnetic field for given fluid models and these synthesized echo trains are inverted by the hybrid method. The inversion results are consistent with given models. Moreover, the numerical simulation of various fluid models such as the gas-water, light oil-water, and vicious oil-water models were carried out with different echo spacings （TEs） and Tws by this hybrid method. Finally, the influences of different signal-to-noise ratios （SNRs） on inversion results in various fluid models are studied. The numerical simulations show that the hybrid method and optimized observation parameters are applicable to fluid typing of gas-water and oil-water models.