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Systemic Risk in Major Capital Projects: Why Traditional Cost Estimation Fails & How to Fix It

If you’ve been involved in capital project planning, you’ve likely seen it happen—despite detailed estimates, careful risk assessments, and robust controls, cost overruns and schedule delays remain persistent challenges.

The truth is, that most cost overruns are not caused by unexpected project-specific risks but by systemic risks—deeply embedded, structural uncertainties that traditional estimation methods fail to capture.

Machine Learning Methods for Handling Parameter Space Sampling Bias in Unconventional Well Performance Prediction

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.

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