Accurate characterization of rock mechanical parameters in heterogeneous geological formations remains challenging because lithological variations alter the relationship between logging signals and geomechanical responses. Existing approaches, including empirical formulas, pure machine learning models, and feature-augmented learning methods, often compress these variations into a single predictor, which can lead to biased estimates. To address this issue, this study proposes a heterogeneity-aware residual learning framework for rock mechanical parameter characterization from well logs. The method separates the prediction into a global component and a lithotype-conditioned correction, allowing lithological effects to be represented as structured residual behavior. This framework was developed and validated on deep coal-bearing formations in the Ordos Basin. By accounting for lithology-controlled response shifts, it produces predictions that better follow observed geological controls. Cross-well validation demonstrates reduced lithotype-induced bias and stable generalization within the studied formation. Further analysis shows that the performance gain is linked to the residual decomposition structure rather than to the addition of lithotype information alone. Compared with single-stage feature augmentation, the main advantage of the proposed framework is its ability to reduce systematic bias in lithological transition zones while preserving a transparent global–residual structure. Its demonstrated applicability is limited to wells within the studied coal-bearing formation, and broader transferability requires further validation.
Liu et al. (Fri,) studied this question.