BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Tendenci - The Open Source AMS for Associations//Tendenci Codeba se MIMEDIR//EN BEGIN:VEVENT DESCRIPTION:--- This iCal file does *NOT* confirm registration.\r\nEvent d etails subject to change. ---\r\nhttps://www.spegcs.org/events/4641/\r\n\r \nEvent Title: Intro to Unstructured Data and Machine Learning in Oil and Gas\r\nStart Date / Time: Nov 22, 2019 13:00 PM US/Central\r\nLocation: Th e Cannon Houston (Satellite Location)\r\nSpeaker: Alec Walker\r\nGoogle\r\ nhttp://maps.google.com/maps?q=675+Bering+Drive+Suite+200,Houston,Texas,77 057\r\n\r\nForecast\nhttp://www.weather.com/weather/monthly/77057\r\n\r\nP LEASE register using Eventbrite as well as SPE: https://www.eventbrite.com /e/78492066885\r\n \r\nThis course is aimed at people without a background in machine learning, software engineering, or data science. The purpose o f the course is to help people who are immersed in the oil and gas industr y to gain a practical understanding of what unstructured data is, what val ue there is in it, how it can be utilized, and why this is now relevant. M uch of unstructured data mining is based on machine learning, so this cour se also seeks to instill memorable intuitive understanding of machine lear ning. This course does not require any use of computers. The course consis ts of these segments:\r\n1) The differences between structured and unstruc tured data will be explored, and emphasis will be placed on why unstructur ed data is crucial to firms in the oil and gas industry. The drastic ineff iciencies created by mismanagement of unstructured data will be given cont ext by the growth in private equity backing, the lower hydrocarbon price e nvironment, the proliferation of data sources, and the aftermath of the bi g crew change.\r\n2) Attendees will engage in an interactive simulation of a typical oil and gas workflow involving structured and unstructured data .\r\n3) Incumbent solutions, including data file structuring projects and off-the-shelf enterprise search tools, will be considered. Each will be ev aluated based on its ability to return accurate and relevant information t o users in a useful format and at speeds that do not inhibit seamless oper ations.\r\n4) Attendees will engage in an interactive simulation of each o f the two typical incumbent solutions to handle unstructured data.\r\n5) T he basics of machine learning will be explained in simple and intuitive te rms, including a few examples.\r\n6) Attendees will engage in an interacti ve application of machine learning to solve a problem. This application wi ll be a simulation not requiring the use of any electronics.\r\n7) Three c ase studies will be explored showing results and highlighting value added of a new (to oil and gas) class of solution. The first study involved impl ementation of a tool to help workers navigate historical reports to extrac t knowledge to make better decisions in real time. The second study involv ed implementation of a tool to serve as a surrogate for a retiring subject matter expert so that less experienced employees could still get good ans wers to questions. The third case study has not been completed, yet, but i t involves simply using a tool to automatically fill out missing fields in a database from data scattered across unstructured reports.--- This iCal file does *NOT* confirm registration.Event details subject to change. ---\ r\n\r\n--- By Tendenci - The Open Source AMS for Associations ---\r\n UID:uid4641@spegcs.org SUMMARY:Intro to Unstructured Data and Machine Learning in Oil and Gas DTSTART:20191122T190000Z DTEND:20191123T000000Z CLASS:PUBLIC PRIORITY:5 DTSTAMP:20240329T135145Z TRANSP:OPAQUE SEQUENCE:0 LOCATION:The Cannon Houston (Satellite Location) X-ALT-DESC;FMTTYPE=text/html: