Computational and Mathematical Modelling
The idea of implementing mathematical modelling to optimize oil and gas production dates back to the 1960s. Many analytic computer tools for studying real-world problems are available in mathematical modelling. Mathematical and computer models enable researchers to investigate complicated systems and natural phenomena that would otherwise be prohibitively expensive, risky, or impossible to investigate through direct investigation. EOR simulations use mathematical and computer models to develop ideal experiments, lowering oil production costs and times. An optimization strategy for increasing gas output from natural gas reserves has been presented. The approach is based on a mathematical model.
- Mathematical Models for Oil Reservoir Simulation
- Experiment Validation
- Computational Tools
Committee Members
Selim Sanad Shaker
Geopressure Analysis Services, United States
Saim Memon
Sanyou London Pvt Ltd, United Kingdom
Nur Hassan
Central Queensland University, Australia GOPE 2026 Speakers
Selim Sanad Shaker
Geopressure Analysis Services, United States
Nur Hassan
Central Queensland University, Australia
Matthew Godfrey
Enventure Global Technology, United States
Barbara Diaz
Automa Srl, ItalySubmit your abstract Today
Title : The Vacuum Insulated Heatable Curtain (VIHC): From conceptual invention to market deployment as a cost-effective dual solution for window heat loss reduction and localised radiant comfort
Saim Memon, Sanyou London Pvt Ltd, United Kingdom
Title : Hydrogen production from depleted or unproductive oil and gas reservoirs
Cleveland M Jones, Fronteira Energia Ltda, Brazil
Title : Predicting drilling challenges and hazards due to subsurface pressure’s drifting
Selim Sanad Shaker, Geopressure Analysis Services, United States
Title : Transforming waste plastic into hydrogen: Progress, challenges, and future directions in pyrolysis-based integrated pathways
Nur Hassan, Central Queensland University, Australia
Title : Novel expandable liner hanger platform for advanced liner drilling and reaming
Matthew Godfrey, Enventure Global Technology, United States
Title : From empirical decline to intelligent forecasting: A hybrid deep learning framework embedding arps physics for unconventional tight-gas reservoir production prediction
Emmanuel Chibueze Obasi, University of Wyoming, United States