Published paper in Journal of Natural Gas Science and Engineering on evaluation of production drivers in the Marcellus Shale using machine learning approaches

Artificial intelligence and machine learning (ML) are being applied to many oil and gas (O&G) applications and seen as novel techniques that may facilitate efficiency gains in exploration and production operations. Significant improvements in that regard are likely to occur when ML can be applied to evaluate O&G challenges with inherent synergies that may have otherwise not been evaluated concurrently. This study introduces an ensembled framework that couples a data-driven ML predictive model capable estimating a productivity indicator for unconventional O&G horizontal wells that correlates to estimated ultimate recovery (EUR) with a well design optimization approach that maximizes productivity. 


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