Direct Fractal Characterization of Shale Hydrocarbon Content from NMR T1-T2 Echo Train Data

Accurate estimation of hydrocarbon content is a critical component of shale reservoir evaluation. Although nuclear magnetic resonance (NMR) T₁-T₂ measurements are highly sensitive to fluid properties, conventional assessments of hydrocarbon content typically rely on empirical interpretation charts or supplementary experiments, which limit their quantitative reliability and practical applicability. In this study, we propose a novel fractal characterization method based on NMR T₁-T₂ measurements for quantitative evaluation of hydrocarbon content in shale reservoirs. To mitigate the uncertainty introduced by T₁-T₂ spectral inversion, fractal parameters are directly extracted from the original NMR echo train data, bypassing the inversion process entirely. Numerical simulations demonstrate that the echo-based fractal parameters exhibit significantly enhanced sensitivity and discrimination capability with respect to hydrocarbon content when compared with fractal parameters derived from inverted T₁-T₂ spectra. Core-scale experiments further validate that the proposed fractal dimension effectively differentiates movable hydrocarbons from pyrolytic hydrocarbons in shale formations. The proposed method provides a robust, efficient, and inversion-independent approach for shale hydrocarbon content evaluation, offering strong potential for both laboratory studies and field-scale NMR applications.