Data-driven Virtual Flow Meter

About the Session

Masoud is a Data Scientist and Senior Analytics Engineer at Oxy, specializing in machine learning applications for oil and gas production forecasting, event detection, and anomaly identification. With deep expertise in petroleum engineering and advanced analytics, he bridges data science and field operations to drive smarter, data-driven decision-making.

Before joining Oxy, Masoud worked at Halliburton, where he contributed to multiple AI-driven optimization projects enhancing production efficiency and operational reliability. He holds both a PhD and MSc in Petroleum Engineering from Texas A&M University, bringing academic rigor and industry insight to the evolving landscape of digital transformation and predictive analytics in energy.

Key Topics

01 Introduction to Physical Meters vs. Virtual Flow Meters (VFM): Understanding the differences, advantages, and applications of VFMs for accurate production flow estimation in the oil and gas industry.

02 Machine Learning and Deep Learning Modeling for VFM: Exploring how AI-driven models enhance real-time monitoring, predictive accuracy, and operational efficiency.

03 Automated Workflow Pipeline: Leveraging automation to streamline data processing, model deployment, and continuous optimization for scalable VFM implementation.

For more on this conference or to access the session, reach out to us at info@ptnevents.com.