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 16 Sep 2021 09:11 AM by  Patrick Ng
CCUS - from an Independent's perspective
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Patrick Ng
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16 Sep 2021 09:11 AM
    This AAPG Pivoting 2021 Webinar took place 09.15.2021. Link to the video recording will be added when available. Watch this space.

    Data - the challenge and opportunity to "play" with satellite imaged infrared data is posted on Xrathus (sign up and access).

    https://xrathus.com/x/hub/challenge...4/overview

    Get set and get started.

    Abstract -

    Until recently, Enhanced Oil Recovery (EOR) projects utilizing CO2 have primarily been the exclusive bailiwick of large operators with deep pockets. However, with the advent of Carbon Capture, Use, and Storage (CCUS), the landscape could be changing. Historically, the majority of CO2 supplies have come from Natural Sources. Therefore, supplies were limited, volumes controlled by a few players, and transportation was restricted to select corridors.



    Now, that situation may be changing, thanks to new government initiatives to remove CO2 from the atmosphere. Emitters of CO2 have tax incentives that can help pay for the capture, transport, and underground storage of CO2 from man-made (anthropogenic) sources. This opens the possibility for smaller operators, including independents, to take advantage of the same EOR technology and apply it on a smaller scale.



    During this talk, we will give a practical and interactive/immersive look at how independents can target potential CCUS opportunities near their area of interest at this smaller scale. The root of this new technique is a previously unavailable resource: flare gas data. We will demonstrate using new online data/tools that advance our understanding and ease the decision-making process.



    Soon, this same methodology may be applicable beyond CCUS applications. The process can be extended to address challenges in Energy Transition, such as:

    1. Hydrogen generation
    2. Use of waste heat
    3. Electricity micro-generation

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