Reduce Your Risk by Quantifying Geologic Uncertainty through Brownfield Optimization & Probabilistic Forecasting
This webinar explores how CMG software is utilized to identify probable realizations during the history match phase and how this uncertainty is applied to develop probabilistic and robust optimal forecasts.
AbstractThe following main points are covered:
- New history matching methods to incorporate uncertainty
- Workflows to carry multiple history matched models through forecasting and optimization studies
- Easy-to-use tools in CMOST AI to generate, organize and visualize complex multi-realization studies
Jason Close has been with CMG for over a decade, with experience in North America and Southeast Asia. His expertise includes Heavy Oil and EOR Technology with an emphasis on machine learning and optimization. He holds a Bachelor of Science in Petroleum Engineering from the Colorado School of Mines, is a practicing Professional Engineer and is an APEGA Responsible Member for CMG.
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