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/3055/\r\n\r \nEvent Title: Northside: Lessons Learned From Data Mining in Unconvention al Reservoirs\r\nStart Date / Time: Nov 10, 2015 11:30 AM US/Central\r\nLo cation: Greenspoint Club\r\nSpeaker: Randal LaFollette\r\nGoogle\r\nhttp:/ /maps.google.com/maps?q=16925+Northchase+Dr.,Houston,Texas,77060\r\n\r\nFo recast\nhttp://www.weather.com/weather/monthly/77060\r\n\r\nThe task of id entifying key production drivers in unconventional reservoirs remains chal lenging, even after decades of exploration and production in North America during which tens of thousands of horizontal unconventional wells have be en drilled and completed. Tens to hundreds of variables, categorized as re servoir quality, well architecture, completion, stimulation, and productio n metrics, are involved and there are many different interrelationships am ong the variables to be considered. Further, formation evaluation is typic ally minimal and there are unknown variables in the system that can only b e guessed at, ignored, or proxied.\r\n \r\nThe author&rsquo\;s team has co mbined Geographical Information Systems (GIS) analysis and multivariate an alysis using boosted regression trees for improved data mining results as compared to univariate methods. The purpose of this lecture is to discuss key elements of data mining in unconventional reservoirs, in order to rais e awareness of cutting-edge statistical tools and methods being brought to bear in the industry. The presentation will provide highlights of real wo rld examples of data mining projects in three different shale plays.\r\n \ r\nIf there were only one idea for audiences to take away from the lecture , it would be that exploiting unconventional reservoirs is a highly comple x task with many moving parts and data mining is a needed tool to be appli ed to better understand the importance of specific well productivity drive rs. Another way to say it is that the talk is intended to provide the audi ence with improved statistical methods for the &ldquo\;statistical&rdquo\; plays so that multi-million dollar decisions can be truly data-driven.--- This iCal file does *NOT* confirm registration.Event details subject to c hange. ---\r\n\r\n--- By Tendenci - The Open Source AMS for Associations - --\r\n UID:uid3055@spegcs.org SUMMARY:Northside: Lessons Learned From Data Mining in Unconventional Reservoirs DTSTART:20151110T173000Z DTEND:20151110T190000Z CLASS:PUBLIC PRIORITY:5 DTSTAMP:20240329T144726Z TRANSP:OPAQUE SEQUENCE:0 LOCATION:Greenspoint Club X-ALT-DESC;FMTTYPE=text/html:
The task of iden tifying key production drivers in unconventional reservoirs remains challe nging, even after decades of exploration and production in North America d uring which tens of thousands of horizontal unconventional wells have been drilled and completed. Tens to hundreds of variables, categorized as rese rvoir quality, well architecture, completion, stimulation, and production metrics, are involved and there are many different interrelationships amon g the variables to be considered. Further, formation evaluation is typical ly minimal and there are unknown variables in the system that can only be guessed at, ignored, or proxied.
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< span style="color: black\; font-family: 'Verdana',sans-serif\; font-size: 10pt\;">The author&rsquo\;s team has combined Geographical Information Sys tems (GIS) analysis and multivariate analysis using boosted regression tre es for improved data mining results as compared to univariate methods. The purpose of this lecture is to discuss key elements of data mining in unco nventional reservoirs, in order to raise awareness of cutting-edge statist ical tools and methods being brought to bear in the industry. The presenta tion will provide highlights of real world examples of data mining project s in three different shale plays.
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If there were only one idea for audiences to take away from the lecture, i t would be that exploiting unconventional reservoirs is a highly complex t ask with many moving parts and data mining is a needed tool to be applied to better understand the importance of specific well productivity drivers. Another way to say it is that the talk is intended to provide the audienc e with improved statistical methods for the &ldquo\;statistical&rdquo\; pl ays so that multi-million dollar decisions c an be truly data-driven.