<p>Low-field nuclear magnetic resonance (NMR) technology serves as a critical tool for laboratory core analysis and downhole formation logging evaluation, providing essential insights into subsurface reservoir characterization. However, measurement discrepancies arising from varying testing conditions, particularly in shale oil reservoirs, significantly limit the universal application of NMR technology for fluid distribution characterization and systematic correlation analysis. These limitations directly impact the accurate interpretation of logging data, the precise assessment of in situ hydrocarbon resources, and effective guidance for exploration. To address these challenges, this paper proposes an innovative Gaussian function affine transformation model (GFATM) that establishes correlations between laboratory and logging NMR T₂ spectra. The model systematically integrates three critical factors: (1) echo spacing variations, (2) pore fluid escape effects, and (3) differential NMR responses between shale oil and water systems. Through quantitative analysis of geometric characteristics of fluid distribution (peak position, width, and height), the GFATM achieves affine spatial conversion of pore fluid signals between laboratory and logging NMR T₂ spectra. A robust power function relationship between T₂ relaxation times measured under laboratory and logging conditions enhances identification of multifluid phases in logging NMR through empirical conversion relationships derived from laboratory shale sample data. Comparative analysis confirmed strong consistency between transformed and original NMR T₂ spectra, validating the method’s accuracy and engineering applicability for reservoir fluid identification.</p>