From Laboratory Measurements to AI-Driven Insights: Predicting Shaped Charge Performance with Advanced Machine Learning

The accurate estimation of the perforation length is very vital to improve fluid flow as well as the management of charges. Traditional methods, including empirical correlations, analytical models, and API 19B surface tests, suffer from significant limitations in their scope, require frequent recalibration, and fail to capture the complex physics governing shaped charge penetration. This study develops and validates machine learning models for perforation length prediction using a comprehensive dataset of 1648 API 19B standardized tests encompassing diverse gun configurations, explosive properties, and completion parameters. The dataset was partitioned into 1318 tests for model training and hyperparameter optimization, with 330 independent tests reserved for blind validation. Ten regression algorithms were systematically evaluated, with XGBoost demonstrating superior performance, achieving an R2 coefficient of 0.956 on blind validation. Feature importance analysis revealed explosive weight as the dominant predictor, followed by temperature rating. The application of machine learning models offers an accurate, easier, instantaneous during planning and design workflows, and cheaper way of estimation as compared to traditional methods.