This map shows the location of the main gas pipelines and the main sources of methane emissions related to the oil and gas industry..
Q: what can we learn from emission data analytics?
A: get a feel for the size of the challenge, and better quantify the impact under different COP26 scenarios.
To wet the "data" appetite, we have setup a compact data analytics exercise (with satellite-derived flaring data from ND, NM and TX). The interactive simulation is collaborative and takes place on Xrathus, a new collaboration platform, which AAPG members can access free of charge.
1) To play, simply sign up with https://xrathus.com/
2) Navigate to Projects, then Challenges
3) Access "Digital Exploration for the Energy Transition" section
4) Launch Workspace (no explicit virtual machine provision required, just choose CPU and go). We aspire to deliver a frictionless learning experience for all.
5) Warm up (optional) - use the Jupyter Notebook to QC flaring input. Provided as learning tool (see attached).
The rest is up to our imagination and experimentation from descriptive statistics to deep learning prediction.
Going Live - Members of the Deep Learning TIG are participating in Energy In Data - Feb 20 - 23, 2022. Energy In Data is a a three-society effort (SEG, AAPG, SPE) and includes an ESG track. Hence this interactive simulation / GIS activity to locate flared natural gas in order to mitigate and eliminate them is a part of the ESG track.
Building Community - Energy In Data is a conference, and also an on-going community, which will come together to collaborate to discuss how to solve energy industry challenges, which will be identified in breakout groups on Monday, Tuesday, and Wednesday afternoons during the conference.
Vamos Challenge for Energy in Data - make a visible impact is to make the ultra emitters map go dark.