核磁自旋回波串的液体分量分解快速反演法(英文)

该文叙述核磁自旋回波串的液体分量分解快速反演法.此方法假定液体,无论是在散装形式或饱和多孔介质中,可以用一个或一组核磁弛豫线形来表征.对一维核磁共振的拉普拉斯反演,它可以是预先确定的一个或一组T2或T1分布.对二维核磁共振的拉普拉斯反演,它可以是一个或一组预先确定的( D, T2)或( T1, T2)二维分布.对三维核磁共振的拉普拉斯反演,它可以是一个或一组预先设定的( D, T1, T2)三维分布.这些预先确定的线形,可以是高斯、B样条或预先由实验或经验确定的任何线形.这种方法可以显着降低核磁共振数据反演的计算时间,特别是从石油核磁共振测井采集的多维数据反演,它不需牺牲反演所得的分布的平滑性和准确性.这种方法的另一个新应用是作为一种约束求解方法来过滤相邻深度所采集的数据噪音.核磁共振测井的噪音信号,往往造成在相邻深度的同一岩性岩层有不同的T2分布.在此情况下, T2分布就不能用来识别岩性.通过非一般的矩阵操作,作者成功实现了对相邻深度的回波串实施约束求解方法,从而使得T2分布成为一种可靠的岩性识别指标.

Fast NMR CPMG Data Inversion Using Fluid Component Decompositions

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.