Prediction accuracy for extended-reach laterals and high-intensity completions is improved by applying a Gaussian Process Regression (GPR) model with a Matérn kernel that accounts for parameter space sampling bias. The method adjusts predictions based on local data density and provides uncertainty quantification, increasing reliability in underrepresented regions of the design space. Applied to wells from the Bakken Formation, the model outperforms traditional approaches and supports confident forecasting for non-standard development designs.