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Emerging Opportunities with Data Analytics and Machine Learning in Subsurface Modeling

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Course Credit: 0.15 CEU, 1.5 PDH

Every subsurface-oriented company that I visit is interested in growing new internal capabilities to add value with data analytics and machine learning. The geosciences have a long history of working with large, complicated datasets. In fact, we have been working with ‘big data’ for decades! We have an opportunity to build upon our strong foundation of geoscience and engineering interpretation and physics, along with spatial mapping, probability and geostatistics competencies, to augment our workflows with new emerging data analytics and machine learning methods.

Due to the unique challenges of the subsurface, for example data sparsity and uncertainty, many current data analytics and machine learning methods are not ready for subsurface application off-the-shelf. Given these gaps, a suite of new emerging, enabling technologies are required to fully realize the value of data analytics and machine learning to enhanced geoscience and engineering capabilities and impact. This supports optimum subsurface development and environmental stewardship decision making.

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Course Chapters

  • 1Emerging Opportunities with Data Analytics and Machine Learning in Subsurface Modeling - Chapter 1
    Media Type: Video

Credits

Earn credits by completing this course0.15 CEU credit1.5 PDH credits

Speakers

Dr. Michael Pyrcz