Date: Thursday April 8th, 2021
Speaker: Juliana Leung
Associate Professor, University of Alberta
A link will be sent to your preferred email the day of the event
Time: 12:00 PM - 1:00 PM MST
SPE Member: Free Non- Member: $20
This talk will explore how data-driven modeling workflows can be used to forecast production, infer chamber development, and analyze the impact and distribution of flow barriers from production time-series data. Numerous studies utilizing both synthetic data from simulation results and data gathered from the public domain will be discussed. We will explore a few examples of how machine learning and data mining can be integrated to answer some of these questions:
This type of analysis could complement many existing monitoring techniques (e.g., seismic) to deliver a more comprehensive inference of the distribution of reservoir heterogeneities in these highly complex bitumen recovery operations.
Juliana Leung is an associate professor in the Civil and Environmental Engineering Department at the University of Alberta. She holds a BSc degree in chemical engineering from the University of Calgary, and MS and Ph.D. degrees in petroleum engineering from the University of Texas at Austin. Her professional experiences include working as a reservoir engineer for over two years at Shell Canada Ltd. and summer internships at the Sandia National Laboratories and ExxonMobil Upstream Research Company in Houston. Her research interests are in the areas of data analytics and modeling of multi-scale flow processes in heterogeneous subsurface porous media. She is an associate editor of the Journal of Petroleum Science and Engineering.
April 8, 2021
From 12:00 PM to 1:00 PM