Emerson Rock Type Classification with Machine Learning Gains Top Spot in Hart Energy’s 2019 Meritorious Awards for Engineering Innovation

Posted by Samhita Shah on Aug 12, 2019 10:06:41 AM RSS Share Post

RTC_MEA_AwardThe results are in! Emerson's E&P Software, Rock Type Classification (RTC) with Machine Learning, is the winner of the 2019 Hart Energy Meritorious Awards for Engineering Innovation (MEA) in the category of Exploration/Geoscience. The MEA program recognizes new products and technologies that demonstrate innovation in concept, design and application.

“Our magazine and its predecessors have consistently honored technical innovation that allows our industry to overcome seemingly impossible challenges,” said Jennifer Presley, Executive Editor of E&P. “The Meritorious Awards for Engineering Innovation reflect the best of the best in technological advancement.”

The sheer volume of well and seismic data that needs to be analyzed today has made Machine Learning an effective approach for transformation and analysis of subsurface data. Automated Machine Learning produces outputs in minutes or hours rather than months or years.  

Emerson’s Rock Type Classification with Machine Learning combines the latest innovations in geoscience, algorithms and statistical models to help oil and gas operators overcome the limitations of traditional methods for predicting facies and rock types from seismic data.  This technology uses a proprietary supervised Machine Learning approach called Democratic Neural Network Association (DNNA) to reconcile multiple data sets to predict facies away from the wellbore. In this approach, an “ensemble” of many neural networks runs in parallel, simultaneously learning from the multi-resolution well bore and seismic data using different strategies and associations.

The outcome of this process is a probabilistic facies model description of the reservoir, associated maximum probability, and the probability relative to each facies.  This results in less guesswork when quantifying uncertainty in rock type distribution. Results are interactively generated in a 2D and 3D environment for in-depth analysis, and are reservoir simulation ready. The outcome is critical for reservoir geologists and engineers to better understand reservoir behavior.

Since Machine Learning integrates data of different resolutions and domains, it becomes a collaboration agent where geologists, reservoir engineers, and geophysicists can work together to ensure that disparate data is calibrated, and results validated. It is ideal for Cloud implementation.

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Tags: geoscience, Oil & Gas software, rock type classification, machine learning in oil and gas, lithofacies, facies classification