Geologically-inspired and mutation-aware driven well log density reconstruction method for complex heterogeneous formations

To address the issue of data distortion in traditional well log density reconstruction methods caused by harsh borehole environments in complex heterogeneous formations, and the challenge for general machine learning models to capture high-frequency abrupt change features due to the lack of geophysical constraints, this paper proposes a density curve reconstruction framework integrating geologically-inspired feature engineering and a mutation-aware mechanism. The method first constructs a multi-dimensional enhanced feature space covering sedimentary cycle characteristics and fluid properties through multi-scale geologically-inspired analysis, utilizing robust range normalization to eliminate interference from outliers. Subsequently, a mutation-aware driven algorithm based on the gradient change rate is innovatively designed. By dynamically adjusting loss weights, it resolves the drawback where traditional smoothing algorithms tend to erase formation interface information. Finally, geological prior constraint terms are embedded into the XGBoost architecture to achieve multi-objective optimization that balances data-driven accuracy with consistency in geological laws. Under leave-one-well-out cross-validation over ten wells, the proposed method achieves a mean R² of 0.98, an RMSE of 0.029, and an MAE of 0.022. Experimental results demonstrate that the proposed method not only improves reconstruction accuracy but also significantly enhances the physical consistency of the results. Furthermore, it precisely identifies thin interbedded boundaries, providing highly reliable basic data support for the refined evaluation of oil and gas reservoirs.