Renowned scientists, engineers and researchers from around the world to gather July 10-16, 2017 in Austin, TX to share and collaborate to advance scientific computing tool
AUSTIN, TX – June 6, 2017 – Enthought, as Institutional Sponsor, today announced the SciPy 2017 Conference will be held July 10-16, 2017 in Austin, Texas. At this 16th annual installment of the conference, scientists, engineers, data scientists and researchers will participate in tutorials, talks and developer sprints designed to foster the continued rapid growth of the scientific Python ecosystem. This year’s attendees hail from over 25 countries and represent academia, government, national research laboratories, and industries such as aerospace, biotechnology, finance, oil and gas and more.
“Since 2001, the SciPy Conference has been a highly anticipated annual event for the scientific and analytic computing community,” states Dr. Eric Jones, CEO at Enthought and SciPy Conference co-founder. “Over the last 16 years we’ve witnessed Python emerge as the de facto open source programming language for science, engineering and analytics with widespread adoption in research and industry. The powerful tools and libraries the SciPy community has developed are used by millions of people to advance scientific inquest and innovation every day.”
Special topical themes for this year’s conference are “Artificial Intelligence and Machine Learning Applications” and the “Scientific Python (SciPy) Tool Stack.” Keynote speakers include:
What: Presentation, demo, and Q&A with Brendon Hall, Geoscience Product Manager, Enthought
Who should watch this webinar:
- Oil and gas industry professionals who are looking for ways to extract more value from expensive science wells
- Those interested in learning how artificial intelligence and machine learning techniques can be applied to core analysis
Geoscientists and petroleum engineers rely on accurate core measurements to characterize reservoirs, develop drilling plans and de-risk play assessments. Whole-core CT scans are now routinely performed on extracted well cores, however the data produced from these scans are difficult to visualize and integrate with other measurements.
Virtual Core automates aspects of core description for geologists, drastically reducing the time and effort required for core description, and its unified visualization interface displays cleansed whole-core CT data alongside core photographs and well logs. It provides tools for geoscientists to analyze core data and extract features from sub-millimeter scale to the entire core.
In this webinar and demo, we’ll start by introducing the Clear Core processing pipeline, which automatically removes unwanted artifacts (such as tubing) from the CT image. We’ll then show how the machine learning capabilities in Virtual Core can be used to describe the core, extracting features such as bedding planes and dip angle. Finally, we’ll show how the data can be viewed and analyzed alongside other core data, such as photographs, wellbore images, well logs, plug measurements, and more.
What You’ll Learn:
- How core CT data, photographs, well logs, borehole images, and more can be integrated into a digital core workshop
- How digital core data can shorten core description timelines and deliver business results faster
- How new features can be extracted from digital core data using artificial intelligence
- Novel workflows that leverage these features, such as identifying parasequences and strategies for determining net pay
||Brendon Hall, Enthought
Geoscience Product Manager and Application Engineer