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This webinar presents a novel new technique – Automated Rock Physics Model Selection – that leverages the expectation-maximization algorithm to simultaneously derive facies, rock physics models and parameters, and associated compaction trends directly from measured well log data. The RokDoc implementation provides a consistent, repeatable, and objective means of classifying log data, calibrating RPMs, and deriving the inputs for facies-based inversions, thus overcoming interpreter bias, whilst significantly speeding up the well log interpretation process. RokDoc Rock Physics Machine Learning (RPML)
This webinar explains and demonstrate how the new function works, highlighting its strengths in storing and propagating expert knowledge through Petrophysics, Rock Physics, and Reservoir Characterization workflows. The viewer will gain a good understanding of the relevant applications and tasks that this new Deep QI functionality can assist with and optimize as well as an opportunity to try it on their own data.
