In a sector where energy efficiency directly impacts cost and sustainability, traditional forecasting methods are no longer enough. At our upcoming conference, Khalid S. Babtain, Operations Unit Head at Aramco, will present a transformative case study from the Hawiyah Gas Plant, showcasing how machine learning is reshaping energy forecasting in oil and gas facilities.
Khalid brings over six years of hands-on expertise in gas operations, including dehydration, compression, and hydrocarbon stabilization. A Certified Energy Manager (CEM) and ISO-certified lead assessor, Khalid has earned international recognition, including the Young Energy Professional of the Year Award by AEE for the Middle East Region.
His session will explore the shift from outdated Excel-based linear regression models to advanced machine learning-powered Energy Demand Forecasting Solutions. These digital tools are engineered to navigate the complex, ever-changing environment of oil and gas processing. The results? Reduced energy consumption, enhanced cost-efficiency, and better environmental outcomes.
Through a detailed case study of the Hawiyah Gas Plant, Khalid will walk attendees through the implementation process, showcasing how this solution bridges the gap between conceptual engineering and practical, digital innovation.
Key Topics :
- Limitations of Traditional Models – Why historical data and Excel-based regressions are no longer sufficient
- AI-Powered Forecasting – Using machine learning and data analysis to predict energy needs in dynamic environments
- Digital Transformation in Practice – Turning engineering concepts into tangible, energy-saving solutions
- Hawiyah Gas Plant Case Study – Real-world impact: energy consumption reduction, cost savings, and sustainability gains
For more on this conference or to access the session, reach out to us at info@ptnevents.com.
This content is copyrighted by PTN Events Private Limited. Any recording and posting of this content is strictly prohibited.
