Pore pressure prediction is a critical aspect of subsurface exploration in the oil and gas industry, focusing on estimating the pressure exerted by fluids within rock formations. Accurate pore pressure predictions are essential for drilling operations as they help prevent wellbore stability issues, kicks, and blowouts. Geoscientists and drilling engineers utilize seismic data, well logs, and geological information to model and predict pore pressure variations across subsurface formations. Methods like Eaton's Ratio, Eaton's Method, and Bowers' Model are commonly employed to estimate pore pressure by analyzing seismic velocity and density data. Real-time monitoring during drilling, using downhole sensors and cuttings analysis, also contributes to ongoing pressure assessment. Pore pressure prediction is particularly crucial in unconventional reservoirs and deepwater drilling, where subsurface conditions can be challenging and variable. Inaccurate predictions can lead to drilling complications, increased operational costs, and potential environmental hazards. Therefore, continual advancements in technology, including advanced data analytics and machine learning algorithms, are applied to enhance the precision of pore pressure predictions. The integration of diverse data sources and sophisticated modeling techniques contributes to more reliable predictions, supporting safer and more efficient drilling practices in the dynamic realm of subsurface exploration.
Title : An integrated and scalable workflow for evaluating polymer flooding and conformance control strategies in heterogeneous waterflooded reservoirs
Farah Faiz, SLB Digital, Colombia
Title : A novel geomechanical workflow for well design optimization through a new approach in friction coefficient estimation, a case study from West Delta Deep Marine Concession, Mediterranean Sea, Egypt
Akram Sami Elsherbiny Ali Hussein, Rashid Petroleum Company, Egypt
Title : Development and validation of an in-house Python workflow for pressure transient analysis of oil wells against commercial software (KAPPA Saphir)
Waad Faraj Mohamed Elarbi, Waha Oil Company, Libya
Title : HALP system: An edge-AI and two-tier architecture for cost-effective structural health monitoring in extreme industrial environments
Menna Allah Abdelnabi Mohamed Ali, Higher Institute for Engineering in Belbeis, Egypt
Title : Multi-scale evaluation of mechanical properties of the geomaterials
Hem Bahadur Motra, University of Kiel, Germany