This study presents a metrologically traceable, microscale measurement framework integrating Confocal Laser Scanning Microscopy (CLSM), fluorescence spectroscopy, and chemometric modeling for quantitative crude oil density mapping. Thirty-three samples from the Songliao Basin (0. 764-0. 9655 g/cm³) were analyzed under 488 nm excitation with emission spectra collected from 500-794 nm. Baseline correction, smoothing, and normalization improved spectral consistency, while principal component analysis with Hotelling's T² removed two outliers prior to modeling. Support Vector Regression (SVR) outperformed Partial Least Squares Regression (PLSR), achieving a calibration R²cal of 0. 968 (RMSEC = 0. 008) and a prediction R²ₚre of 0. 955 (RMSEP = 0. 012). The 737 nm wavelength exhibited the strongest correlation (r = 0. 748, Pearson correlation coefficient) with aromatic and asphaltene content, linking molecular composition to density variation. Applied to CLSM spectral images of shale thin sections, the SVR model produced micrometre-scale density maps consistent with compositional heterogeneity-light fractions along fracture walls and heavy fractions within fracture interiors. A standalone desktop tool has been developed to enable spectral import, automated processing, and density visualization. This non-destructive, high-resolution approach addresses the limitations of bulk measurements, enabling reproducible, spatially resolved density determination in complex geological matrices and advancing measurement science.
Yang et al. (Fri,) studied this question.