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Deep Learning/Machine Learning Technical Interest Group (TIG)

Deep Learning - Machine Learning TIG
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0 Replies and 393 Views Read This First!      393  0 Started by  Bogdan Michka Welcome to AAPG N.E.T., an online space where you can Network, Engage and Talk. Please complete the following steps before you begin interacting with the discussion boards: 1. Take time to review your profile and privacy settings, paying attention to your display name and photo, as they will be used to identify your posts. 2. Read the Discussion Boards FAQ page to learn the rules and get familiar with the features: http://www.aapg.org/career/aapg-net/discussion-boards-faq — pa...
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09 May 2017 03:37 PM
0 Replies and 12 Views ML - more doing, less guessing  12  0 Started by  Patrick Ng Premise - we all learn AI / ML in different ways, I for one go for Coursera (Prof Andrew Ng's ML series) and BYO apps, as well as online learning nuggets that offer value for no cost. Actionable - starting tomorrow, free sampling intro sessions across ML and data science offering from Daytum, an energy-focused education platform. https://www.eventbrite.com/o/daytum-21726800390 First and foremost, Prof Pyrcz speaks 'geo' and makes these a solid starting point / refresher for anyone embar...
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09 Jun 2021 01:57 PM
0 Replies and 26 Views AAPG-SPE Marriage  26  0 Started by  Patrick Ng We all read this morning AAPG and SPE coming together as one. It accelerates the shift from petroleum to energy. So how can Deep Learning TIG contribute 1. Learn a little more jargons used (may be same term but different context) and communicate better; e.g., using visuals of 3Ps (P10, P50, P90) and simple proxy model (linear fit) may be useful. Click on the map (e.g., sweet spots around Midland Airport) https://www.vue-shale-wells.com/ Update 06.09.2021 - attach...
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25 May 2021 09:22 AM
0 Replies and 37 Views Azure, AWS and GCP Snapshot Q1 2021  37  0 Started by  Patrick Ng Pick up where we left off last post October 23, 2019 (Azure, AWS and GCP snapshot Q3, 2019). Premise - here we shall focus on productivity (min coding max use of ML). Scenario - take a spreadsheet or table like (structured) dataset, where the rows are well API numbers, and columns of location, well depth, lateral length of horizontal wells, formation thickness, porosity, velocity, density, other subsurface properties, etc.). Objective - train ML model to rank some proposed well locat...
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27 Apr 2021 07:44 PM
0 Replies and 43 Views Pivot 2021 - ML, Geothermal and Smart Completions  43  0 Started by  Patrick Ng Learning from other industries - sign up for a free workshop April 28, after hours for flexibility Presentations: John Holbrook, Ph.D., Texas Christian University Geothermal Developments, Solar Energy Storage Sean Marshall and Danny Rehg, Criterion EP Geothermal Lease Evaluation – A Challenge Dan Taranik, Exploration Mapping New and Old Satellite Imagery for Surface Temperature and Emissivity Deborah Sacrey, Auburn Energy Bivariate Statistics on Old East Texas Field for New Pr...
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22 Apr 2021 03:00 PM
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