Deep Learning/Machine Learning Technical Interest Group (TIG)

Applying new analytics, neural networks, computational approaches using structured and unstructured data, and also training neural networks with supervised and unsupervised algorithms. Chaired by Patrick Ng and Andrew Munoz.
Deep Learning - Machine Learning TIG
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Last Post 15 Mar 2021 01:09 PM by  Patrick Ng
First look at well production data - before tackling how well ML models generalize?
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Patrick Ng
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15 Mar 2021 01:09 PM
    Follow up to prior post “AI teaches itself - no manual labels required” 03.08.2021.

    Perspective - day in and day out, geologists, engineers, and analysts are active players in asset evaluation and project economics. 80% of the time spent on generating oil and gas projects: exploring ideas with a map, finalizing investment decisions and picking well locations on a map. And what else we may do the other 20%? Due diligence and risk assessment. A common issue is the time and effort it takes to perform such routine tasks day in and day out. Consider the 80% of time spent on the “creation" of projects and how helpful it would be if we could have more time to work on the 20%. We’d screen more opportunities, drill down to greater details and improve the quality of the final decision. Leading to better return and fewer surprises.

    Actionable and practical tool for a digital journey? Lets start a simple geosearch with well data - vue-shale-wells.com

    For warm up, https://youtu.be/PwuLmSM-zW8

    Ready embarking on the digital journey? By attending focused workshops and sampling across sessions, we get exposure to actionable insights on how to diagnose and home in on the more promising paths to deploy AI and machine learning in projects. And it only takes one click to start that journey this April, i.e.,

    https://energyindata.org/
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