I have a complete dataset from six wells—five for training and one blind well for testing—and I need a publish-ready scientific paper that documents how artificial-intelligence models can predict petrophysical logging curves, specifically GR and DT. Up to now I have relied solely on traditional supervised learning models with nothing more than straightforward normalization, which has produced excellent training metrics but disappointingly poor generalization on the blind well. Your task is to help me turn this into a rigorous study that both improves model performance and tells a compelling story fit for an SPE conference or a journal such as JPSE. I want the manuscript to cover: • Dataset description and quality checks (with attention to well-by-well splits) • Methodology: current supervised baselines plus any additional models you judge valuable—ensemble tree methods, regularized linear algorithms, or even deeper networks—as long as they are justified scientifically • Pre-processing workflow, clearly stating my existing normalization step and any new feature-engineering or outlier-handling strategies you introduce • Experimental design: train on five wells, blind-well validation, cross-validation where appropriate, and metrics such as RMSE, MAE, and R² • Results, error analysis, and discussion focused on why the blind well fails and how the revised approach mitigates overfitting • Conclusions and future work Deliverables will be: 1. A full manuscript in Word or LaTeX, complete with figures, tables, and references. 2. Reproducible Python notebooks (scikit-learn, TensorFlow or PyTorch are fine) and any supporting data files. 3. High-resolution plots suitable for publication. Clear, concise writing and transparent code are essential; the final package should be ready for submission with minimal reformatting.