Quantifying the Incremental Value of XRF Elemental Data for Well-Log-Based TOC Prediction: A Fusion Strategy Evaluated Under Strict Cross-Validation

Abstract

Total organic carbon (TOC) is a key parameter for source-rock evaluation, but laboratory measurements are sparse and do not resolve continuous vertical variation. Conventional well logs provide continuous physical responses, whereas X-ray fluorescence (XRF) data add geochemical information. We evaluated the incremental value of combining these data sources using 58 depth-matched samples from the Yiwan-1 well in the Wangjiawan area, South China. Four well-log variables and six XRF variables selected within each training fold were evaluated with nine models under repeated 5-fold cross-validation (20 repeats). The fused SVR model performed best (RMSE = 0.789 ± 0.267; R = 0.912 ± 0.081). At the repetition level, the absolute RMSE reduction relative to logs alone was 0.430 (95% CI: 0.386–0.478), equivalent to 35.3% (Holm-adjusted p = 1.34 × 10−5). A 500-run permutation test that shuffled complete XRF sample rows gave an empirical p-value of 0.002. In a separate contiguous depth-block analysis with a 0.16 m exclusion buffer, the SVR RMSE decreased from 2.566 to 1.837, corresponding to an improvement of 28.4%, although the benefit was not retained by every linear model. Mo, V, Cr, Cd and Zn were selected in all Pearson-selection folds and had positive mean held-out permutation importance. The results support a complementary contribution from XRF data within this well while also showing that estimated accuracy depends on the validation design.