XGBoost regression for robust acoustic impedance prediction in the absence of density and sonic logs

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) as optimal predictors. Data preprocessing included Isolation Forest-based outlier removal and logarithmic resistivity transformation. The XGBoost regressor - selected for its scalability in handling nonlinear interactions - was trained on 80% of the data, with hyperparameters optimized via cross-validated grid search. Model performance was evaluated using mean absolute error (MAE), root MSE (RMSE), and coefficient of determination (R²). The optimized model achieved an R² of 0.916 (training) and 0.808 (testing), with RMSE values of 718.3 and 1070, respectively. Independent validation on a blind well demonstrated strong generalization (R² = 0.869, RMSE = 981.3), with predicted Z logs showing stratigraphic fidelity and suppression of high-amplitude artifacts inherent to sonic-derived impedance. Compared to empirical methods, the ML workflow eliminates reliance on matrix/fluid constants, accommodates shale volumes > 25%, and mitigates errors from secondary porosity or gas effects. This provides a scalable, cost-effective solution to enhance seismic inversion accuracy in data-scarce or complex lithological settings.

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