Accurate subsurface evaluation is essential for optimizing hydrocarbon recovery while mitigating operational risks. Geomechanics & Reservoir Characterization integrate seismic data, rock mechanics, and fluid interactions to predict reservoir behavior and enhance drilling success. Advanced 3D modeling and machine learning techniques are improving the accuracy of fracture prediction and stress distribution analysis. Understanding formation stability is crucial for wellbore integrity and hydraulic fracturing optimization. Additionally, digital twin technology is transforming real-time decision-making by simulating reservoir conditions under various scenarios. As exploration targets more complex reservoirs, geomechanics plays a pivotal role in reducing uncertainties and enhancing production strategies.
Selim Sanad Shaker
Geopressure Analysis Services, United States
Saim Memon
Sanyou London Pvt Ltd, United Kingdom
Cleveland M Jones
Fronteira Energia Ltda, Brazil
Jia Min Jin
Shanghai Research Institute of Materials, China
Matthew Godfrey
Enventure Global Technology, United States
Javier Garrigos
Tecnicas Reunidas, Spain
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