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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.

The CM-CX Approach: Integrated Construction Management & Commissioning

In this article, we look at the integrated CM-Cx service model alternative—where both functions are managed by a single entity with a unified team and goal—designed to maximize project value, minimize risk, and enhance execution efficiency.

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