CMOST - Going Beyond Today’s
Reservoir Simulation Workflow



Artificial intelligence, machine learning and pattern recognition technology have come a long way, especially during the past decade. The rise in automated and AI-powered data interpretation tools, where repetitive time consuming tasks are delegated to machines and bots, is allowing many businesses to shift their focus to data-driven workflows, leading to comprehensive and efficient decision making. New advancements in CMOST augment traditional reservoir simulation workflows driven by its proxy based algorithms to leverage engineering capabilities, expertise and potential. Learn how CMOST can apply neutral, non-biased data interpretation along with human expertise to generate high-value predictions that guide better decisions.

Alex Novlesky, a Senior Reservoir Simulation Engineer & Consulting Coordinator with CMG, is an expert in the simulation of unconventional oil or gas reservoirs (CBM, shale & tight) including hydraulic fracturing and microseismic. He specializes in the areas of optimization and uncertainty analysis and oversees a variety of simulation projects as a Consulting Coordinator at the Calgary office. Alex holds a Bachelor of Science, Oil & Gas Engineering Degree from the University of Calgary and is in his 10th year at Computer Modelling Group Ltd.

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