This paper presents a reasonable gridding-parameters extraction method for setting the optimal interpolation nodes in the gridding of scattered observed data. The method can extract optimized gridding parameters based...
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This paper presents a reasonable gridding-parameters extraction method for setting the optimal interpolation nodes in the gridding of scattered observed data. The method can extract optimized gridding parameters based on the distribution of features in raw data. Modeling analysis proves that distortion caused by gridding can be greatly reduced when using such parameters. We also present some improved technical measures that use human- machine interaction and multi-thread parallel technology to solve inadequacies in traditional gridding software. On the basis of these methods, we have developed software that can be used to grid scattered data using a graphic interface. Finally, a comparison of different gridding parameters on field magnetic data from Ji Lin Province, North China demonstrates the superiority of the proposed method in eliminating the distortions and enhancing gridding efficiency.
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 longitudi...
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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.
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