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Multidimensional NMR inverse Laplace spectroscopy in petrophysics

机译:岩石物理学中的多维NMR反拉普拉斯光谱

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The characterization of reservoir quality necessitates a good understanding of the pore scale physics of the reservoir, in particular permeability of the formation, wettability, and fluid saturations. Classical interpretation of 1D NMR logging data, a T2 relaxation response, uses a 1D inverse Laplace transform to derive a distribution of relaxation times from the magnetization decay, assuming weak coupling between pores and the fast diffusion limit, in which case the magnetization decay decouples into a sum of exponentials. The distribution is typically split into bound and free water by choosing an arbitrary cutoff, and permeability estimated by using a correlation involving the log mean relaxation time. More recently, 2D inverse Laplace experiments using T1-T2 or T2-D correlations were used to enable fluid typing in the case of partially saturated formations. While fluid typing is considerably more accurate using these 2D methods, mixed wettability and internal gradients can be significant obstacles. To overcome these difficulties, we present in this work higher dimensional measurements such that the relaxation is encoded for additional properties like diffusion or internal gradients (DG20), which enable a more precise characterization of the reservoir environment. In particular, we use pulsed field gradient NMR to encode for the effects of diffusion. Specialized timings further encode for T2 and internal gradient effects. 3D inverse Laplace algorithms are then used to calculate the correlation maps between T2-D-DG20 and T2-D-|G0| of rock cores of varying characteristics. The analysis of higher-dimensional correlation maps has been shown to delineate between different fluids and characterize wettability. Potentially, projections of such delineated populations onto pore size distributions may provide estimates of absolute and relative permeability of the formation.
机译:储层质量的表征需要对储层的孔隙尺度物理学有一个很好的了解,特别是地层的渗透率,润湿性和流体饱和度。一维NMR测井数据的经典解释是T2弛豫响应,它使用一维逆Laplace变换从磁化衰减推导弛豫时间分布,假设孔隙之间的耦合较弱且扩散速度快,在这种情况下,磁化衰减解耦为指数的总和。通常,通过选择任意截止值,将分布分为束缚水和自由水,并通过使用涉及对数平均弛豫时间的相关性来估算渗透率。最近,在部分饱和地层的情况下,使用了T1-T2或T2-D相关性的2D逆Laplace实验被用来进行流体分型。尽管使用这些2D方法进行流体分型要准确得多,但混合的润湿性和内部梯度可能会成为重大障碍。为了克服这些困难,我们在这项工作中提出了更高尺寸的测量方法,以便对弛豫进行编码,以实现诸如扩散或内部梯度(DG20)之类的其他属性,从而可以更精确地表征油藏环境。特别是,我们使用脉冲场梯度NMR来编码扩散的影响。专门的时序进一步编码T2和内部渐变效果。然后使用3D逆Laplace算法计算T2-D-DG20与T2-D- | G0 |之间的相关图。特征各异的岩心。高维相关图的分析显示出了在不同流体之间的轮廓并表征了润湿性。潜在地,这样描绘的种群在孔径分布上的投影可以提供地层的绝对和相对渗透率的估计。

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