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New Techniques for Understanding Drivers of Basin Performance

Predictive models have become highly sophisticated and have an improving track record of helping E&P Operators prioritize capital and identify where to drill the best wells.

IHS Markit has adapted advanced machine learning techniques coupled with novel explicability methods to allow clients to decompose the results of these complex models back to the inputs to better understand and quantify how the input parameters contribute to overall well performance.

In this webinar, after discussing how the rapid shift in the landscape is changing the definition of success for shale players, we will review how Analytics Explorer enables this Factor Contribution Analysis combining IHS Markit data and proprietary client project data (coming from i.e., Kingdom, Harmony, EDM, etc.) to understand how a range of variables - including well design, completion recipe, and location - have impacted the performance of individual operators and subregions in USA basins.


Raoul LeBlanc
Vice President, Energy, IHS Markit

Camilo Rodriguez
Director, Analytics and Data Science, IHS Markit

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