(T2,D)二维核磁共振测井混合反演方法与参数影响分析

摘要

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)测井能同时测量到多孔介质中横向弛豫时间(T2)和扩散系数(D)等信息,利用这两个参数区分流体性质,较一维核磁共振测井技术具有明显的优越性.针对梯度场下的2D NMR测井弛豫机理和数学模型,提出了适用于求解大型稀疏矩阵方程的反演方法—基于非负最小二乘法(LSQR)和截断奇异值分解(TSVD)法的混合算法.为验证方法的有效性,先根据多回波观测模式合成回波串数据,然后再用混合反演算法进行反演,反演得到横向弛豫时间(T2)和扩散系数(D),并构建T2-D二维谱图.结果对比表明,该混合反演算法得到的T2-D二维谱与流体模型一致性好,计算精度均比单一反演方法有较大改善,表明该混合反演方法可用于油气储集层2D NMR测井的反演和流体识别.此外,分别对油水同层和气水同层模型进行了正演模拟和反演实验,系统考察了不同磁场梯度、不同回波间隔组合对反演效果的影响,为2D NMR参数设计提供依据.%One dimensional nuclear magnetic resonance (ID NMR) logging technology is limited for fluid typing. Two dimensional nuclear magnetic resonance (2D NMR) logging can provide more parameters including transverse relaxation time (T2) and diffusion coefficient (D) in multi-pore media, and the fluid typing is realized successfully according to the fluid properties of NMR. So, 2D NMR logging is more advantageous than ID NMR logging. Point to the relaxation mechanism of 2D NMR at gradient field, the echo simulation and inversion of 2D NMR are discussed in detail. And a hybrid inversion algorithm is proposed, which is based on a dampingleast squares method (LSQR) and truncated singular value decomposition (TSVD). For verifying the hybrid inversion method, a series of spin echo trains in an ideal fluid model are firstly simulated with multiple echo spacing (TE) activation in gradient field. Then, these synthesized echo trains are inverted by hybrid inversion method. The inversion results including T2 and D are well consistent with the ideal model presupposed, and the hybrid inversion method is more accurate and efficient than single inversion algorithm from the error analysis and runtime comparison, which indicates that the hybrid method is effective and suits to the inversion of 2D NMR logging. Furthermore, the LSQR-TSVD hybrid method is applied into the inversion tests of oil-water and gas-water models with different observation parameters such as magmatic field gradient and echo spacing group. From the inversion results analysis, the reasonable observation parameters are selected and optimized for fluid typing of (T2, D) 2D NMR Logging.