Under the backdrop of digital subsurface and intelligent field development, together with sustainable development planning, reliable and continuous well-log measurements are increasingly essential for reservoir evaluation and geological interpretation. Density (DEN) logging is critical for reservoir evaluation and geological interpretation, providing fundamental constraints for lithology/porosity-related assessment and integrated subsurface characterization. However, the DEN curve often contains missing intervals or distortions caused by borehole conditions and tool/environmental interference. This study proposes an RF–Transformer framework for DEN reconstruction that couples (i) Random-Forest-based feature screening to suppress redundant or low-contribution channels and (ii) a Transformer encoder with mask-aware self-attention to capture both local fluctuations and long-range depth dependencies. Experiments were conducted on logging data from nine vertical wells in the Lianggaoshan Formation (Sichuan Basin, China) with a unified sampling step of 0.125 m. Under a well-wise split protocol, RF–Transformer achieved RMSE = 0.0126 g/cm3, MAE = 0.0079 g/cm3, R2 = 0.9863, and r = 0.9932, outperforming Random Forest, Decision Tree, KNN, LightGBM, LightGBM–NN, and a base Transformer. The pass rate reached 92.86% under an error tolerance of ±0.02 g/cm3, demonstrating robust reconstruction in long missing sections and lithological transition zones. The proposed workflow provides an effective route for repairing density logs in complex reservoirs and for improving the continuity of multi-log interpretation.
Su et al. (Sat,) studied this question.