Crude oil density prediction and visualization by CLSM fluorescence spectroscopy combined with chemometric methods

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 cm−3) 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 R2_cal of 0.968 (RMSEC = 0.008) and a prediction R2_pre 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.