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0 Replies and 436 Views
Resources: AAPG Energy Insights Podcast 436 0
Started by Bogdan Michka
Did you know AAPG had a podcast AAPG Energy Insights Podcast features people who are defining, influencing and leading today's world of geosciences. Listen to us on the web or on iTunes, Spotify, Google Play Music, Stitcher, and others. iTunes: https://podcasts.apple.com/us/podcast/aapg-energy-insights-podcast/id1460045559mt=2&app=itunes Spotify: https://open.spotify.com/show/7tFNst8Td2HIdVI1lt9GlL Stitcher: https://www.stitcher.com/sfid=...
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29 May 2019 02:07 PM |
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0 Replies and 411 Views
Deep Learning with Geospatial Production Data 411 0
Started by Patrick Ng
Background - often the first thing when we appraise acreage, is to generate a map and look at production in the area of interest. Today we may go further with a quick-n-dirty ML run and get estimate of cumulative production or CUM (over some time period of economic interest). Based on feedback from the AAPG ML and Analytics Workshop, January 2019, one popular request is to create a community of sharing what we have collectively learn from applying ML to our work. We can share things we le...
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27 May 2019 03:33 PM |
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0 Replies and 419 Views
Physics-strong Machine Learning 419 0
Started by Patrick Ng
Background - often we hear people question neural network (NN) as a black box. Recall in geoscience, we often validate an earth model using seismic-well calibration at selected well locations. We prefer a white box. Action - seek black-to-white box pathway, in July 2018, AAPG hosted the inaugural hackathon. https://www.aapg.org/publications/blogs/learn/article/articleid/48323/-domain-meets-deep-neural-networks-hybrid-physics-based-hackathon-for-geoscientists-and-engineers Follow up - ex...
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10 May 2019 09:11 AM |
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0 Replies and 470 Views
Azure, AWS and GCP Snapshot Q1 2019 470 0
Started by Patrick Ng
Snapshot is based on recent experience and what I like about them. Azure - Auto-ML and Power BI link up really compresses cycle time from data to ML and visualize the result. Assume data prep is done, and for test drive case data stored in Excel, running simple Auto-ML classification from setup to execution in 15 min. No programming in R or Python required (think NASCAR, stock cars on steroid). AWS - 1) Alexa really opens the door to AI without explicitly learning how to pro...
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01 May 2019 11:19 PM |
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24 Apr 2019 07:45 AM |
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