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The presentation explores how integrating detailed geological data with machine learning is enhancing gas production forecasting in the Haynesville Shale.
It also compares models using robust geologic attributes versus simple location-based proxies to predict 12-month cumulative gas output per 1,000 lateral feet. Learn how geologic insights improve accuracy, identify overlooked high-potential areas, and outperform spatial-only methods.
Alex Blizzard, senior technical advisor of data and analytics at S&P Global, will be your guide to:
A detailed workflow for constructing and refining multivariate ML models for production forecasting.
A deeper understanding of how geological mapping enhances feature quality and model interpretability.
Quantitative evidence of the limitations of spatial proxies and the value of explicit geologic context.
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