Data-driven decision-making is reshaping artificial intelligence & big data analytics in oil & gas, optimizing operations and minimizing environmental impact. Machine learning algorithms are enhancing seismic data interpretation, improving reservoir management, and predicting equipment failures before breakdowns occur. Predictive analytics is reducing downtime in refineries and drilling operations, while AI-driven process control is improving efficiency and reducing energy consumption. Big data integration is facilitating real-time monitoring of pipelines and offshore platforms, ensuring safety and regulatory compliance. The integration of cloud computing and edge analytics is further enhancing operational efficiency. AI-based automation is expected to play a pivotal role in optimizing oilfield production while reducing emissions.
Title : Transforming waste plastic into hydrogen: Progress, challenges, and future directions in pyrolysis-based integrated pathways
Nur Hassan, Central Queensland University, Australia
Title : Reinventing global inspection: Digitalization and advanced reporting in oil & gas
Javier Garrigos, Tecnicas Reunidas, Spain
Title : Application of vanadium and tantalum single-site zeolite catalysts in heterogeneous catalysis
Stanislaw Dzwigaj, Sorbonne University, France
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 : Geophones for recording acoustic waves in water
Askold Belyakov, Schmidt Institute of Physics of the Earth, Russian Federation
Title : A novel integrated framework catalyst synthesized (Karalite-A) and applied for biodiesel synthesis at room temperature
Chellapandian Kannan, Manonmaniam Sundaranar University, India