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DESCRIPTION:--- This iCal file does *NOT* confirm registration.\nEvent det
 ails subject to change. ---\nhttps://www.swsaapg.org/events/96/\n\nEvent T
 itle: FWGS Student Lecture Series\nStart Date / Time: Dec 17, 2020 12:00 P
 M US/Central\nLocation: Zoom\nSpeaker: Elisabeth Rau\n"The use of machine 
 learning to predict facies in the Duvernay Formation, an unconventional re
 servoir in the Western Canada Sedimentary Basin"\nElisabeth Rau - Baylor U
 niversity Geology Department\n2019-2020 FWGS Scholarship Recipient\n \nWit
 h the rapidly growing and globally expanding inventory of large and comple
 x datasets, i.e., &ldquo\;big data&rdquo\;, machine learning has become a 
 popular data analytics technique within the geoscience community. Here, we
  evaluate the effectiveness of machine learning in the prediction of facie
 s, facies associations, and reservoir versus non-reservoir rock types in a
  proven shale reservoir. The Late Devonian Duvernay Formation is a major p
 etroleum source rock in the Western Canada Sedimentary Basin (WCSB) that w
 ith recent advances in drilling and completions technology has become a ta
 rget for exploration and production. Using the Duvernay Formation as a cas
 e study, both the benefits and limitations of machine learning derived fac
 ies and reservoir quality predictions from wireline logs are evaluated and
  discussed.\nREGISTER HERE\n\n--- This iCal file does *NOT* confirm regist
 ration.Event details subject to change. ---\n\n--- By Tendenci - The Open 
 Source AMS for Associations ---\n
UID:uid96@swsaapg.org
SUMMARY:FWGS Student Lecture Series
DTSTART:20201217T180000Z
DTEND:20201217T190000Z
CLASS:PUBLIC
PRIORITY:5
DTSTAMP:20260421T090509Z
TRANSP:OPAQUE
SEQUENCE:0
LOCATION:Zoom
X-ALT-DESC;FMTTYPE=text/html:<div>--- This iCal file does *NOT* confirm re
 gistration.Event details subject to change. ---</div><h1>Event Title: FWGS
  Student Lecture Series</h1><div>https://www.swsaapg.org/events/96/</div><
 br /><div>When: Dec 17, 2020 12:00 PM US/Central</div><div>Speaker: Elisab
 eth Rau</div><br /><div><h2 style="text-align: center\;">"The use of machi
 ne learning to predict facies in the Duvernay Formation, an unconventional
  reservoir in the Western Canada Sedimentary Basin"</h2> <p style="text-al
 ign: center\;"><strong>Elisabeth Rau - Baylor University Geology Departmen
 t</strong></p> <p style="text-align: center\;">2019-2020 FWGS Scholarship 
 Recipient</p> <p style="text-align: center\;">&nbsp\;</p> <p style="text-a
 lign: left\;">With the rapidly growing and globally expanding inventory of
  large and complex datasets, i.e., &ldquo\;big data&rdquo\;, machine learn
 ing has become a popular data analytics technique within the geoscience co
 mmunity. Here, we evaluate the effectiveness of machine learning in the pr
 ediction of facies, facies associations, and reservoir versus non-reservoi
 r rock types in a proven shale reservoir. The Late Devonian Duvernay Forma
 tion is a major petroleum source rock in the Western Canada Sedimentary Ba
 sin (WCSB) that with recent advances in drilling and completions technolog
 y has become a target for exploration and production. Using the Duvernay F
 ormation as a case study, both the benefits and limitations of machine lea
 rning derived facies and reservoir quality predictions from wireline logs 
 are evaluated and discussed.</p> <h1 style="text-align: center\;"><a href=
 "https://www.fwgs.org/events-1/student-lecture-series" target="_blank" rel
 ="noopener">REGISTER HERE</a></h1></div><div>--- This iCal file does *NOT*
  confirm registration.Event details subject to change. ---</div><div>--- T
 endenci&reg\; Software by <a href="https://www.tendenci.com">tendenci.com<
 /a> - The Open Source AMS for Associations ---</div>
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