关键词: 核磁共振测井, 弛豫分布, 拉普拉斯反演, 液体分类

Abstract:

We developed a “Fluid Component Decomposition”method for fast NMR CPMG data inversion. This method assumes that fluids, either in bulk form or saturated in porous media, have certain predetermined functional shapes, which can be predetermined T₂ or T₁ distributions for 1D NMR inverse Laplace transform problem, predetermined (D, T₂) or (T₁, T₂) distributions for 2D NMR inverse Laplace transform problem, or predetermined (D, T₂, T₁) distributions for 3D NMR inverse Laplace transform problem. These predetermined shapes can be Gaussian, Bspline, or any functions predetermined experimentally or empirically.  This approach significantly reduces the computation time for NMR data inversion especially for multi-dimensional data sets from oil well measurements, without sacrificing the smoothness and accuracy of the inverted distributions.  Such method has a new application as a solution constraint for a group of NMR data with sequential well depths where spurious signals frequently result in dissimilar T₂ distributions for the same rock type.  The successful implementation of this method as a constraint of T₂ components inverted from T₂ echo trains at different depths involves nontrivial matrix manipulations and allows us to use T₂ distribution as a rock type indicator.

Key words: NMR logging, relaxation distribution, Laplace inversion, fluid typing