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<b style="font-family: Calibri, sans-serif; font-size: 11pt; color: inherit; font-style: inherit; font-variant-ligatures: inherit; font-variant-caps: inherit; background-color: ;"><u><span style="font-size:14.0pt; font-family:"Arial",sans-serif; background:yellow">Subject:
PETR SEMINAR SERIES-<span style="color:#1F497D"> November 8</span>, 2019</span></u></b><br>
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<b><span style="font-size:18.0pt; font-family:"Arial",sans-serif; color:black">The Department of Petroleum Engineering Seminar Series:</span></b></p>
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<b><span style="font-size:18.0pt; font-family:"Arial",sans-serif">Friday November 8, 2019</span></b></p>
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<b><span style="font-size:18.0pt; font-family:"Arial",sans-serif; color:black; background:yellow">11am ERP(
</span></b><b><span style="font-size:14.0pt; font-family:"Arial",sans-serif; color:black; background:yellow">now known as Technology Bridge</span></b><b><span style="font-size:18.0pt; font-family:"Arial",sans-serif; color:black; background:yellow">) BLDG 9
Room 135 (</span></b><b><span style="font-size:18.0pt; font-family:"Arial",sans-serif; background:yellow">NEW
</span></b><b><span style="font-size:14.0pt; font-family:"Arial",sans-serif; color:black; background:yellow">9B Auditorium</span></b><b><span style="font-size:18.0pt; font-family:"Arial",sans-serif; color:black; background:yellow">)</span></b><b><span style="font-size:18.0pt; font-family:"Arial",sans-serif; color:black"></span></b></p>
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<b><span style="font-size:14.0pt">Presenter: Xiao-Hui Wu</span></b></p>
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<b><span style="font-size:14.0pt">Topic: Reduced Order Modeling for Accelerating Decisions under Subsurface Uncertainty
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<b><span style="font-size:14.0pt">Abstract:</span></b></p>
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Supporting oil and gas field development decisions is the main objective of reservoir modeling and simulation. Decision support under deep subsurface uncertainty at multiple scales is extremely challenging. In this talk, I summarize the key computational
challenges and present an overview of reduced order modeling techniques used to accelerate the decision support process. Approaches such as numerical homogenization, non-uniform coarsening, reduced physics modeling, and recent advances based on machine learning
are discussed.</p>
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<b><span style="font-size:14.0pt">Bio</span></b></p>
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Xiao-Hui Wu joined ExxonMobil Upstream Research Company in 1997 and is currently a Senior Mathematical Modeling Consultant in ExxonMobil Upstream Integrated Solutions Company. He led multiple research projects in reservoir modeling and performance prediction.
His current interest includes combining machine learning and physics-based models as well as decision making under uncertainty. Wu holds a PhD in Mechanical Engineering from The University of Tennessee and has done post-doctoral studies in Applied Mathematics
at California Institute of Technology. He has been a technical editor for the SPE Journal and served on the SPE Reservoir Simulation Symposium program committee, and he was an SPE Distinguished Lecturer from 2015-2016.
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<b><i> </i></b><span style="font-size:14.0pt; font-family:"Arial",sans-serif"></span></p>
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<b><span style="font-size:14.0pt; font-family:"Arial",sans-serif"> </span></b><b><span style="font-family:"Arial",sans-serif"></span></b></p>
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<b>Anne Sturm.</b></p>
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<b>University of Houston . Cullen College of Engineering</b></p>
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<b>Department of Petroleum Engineering. Academic Advisor</b></p>
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<b><a href="mailto:masturm@uh.edu">masturm@uh.edu</a>. 832.842.4848. petro.egr.uh.edu</b></p>
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