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 : 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 : Enhancing LPG capacity at Rabigh bulk plant: A case study on flow optimization and operational efficiency
Fahad Almehmadi, Saudi Aramco, Saudi Arabia
Title : Unlocking field development through infill well success delivering 3,000 BOPD via integrated dynamic analysis and seismic reprocessing: Lima field, PHE ONWJ
Indra Sanjaya, PHE ONWJ, Indonesia
Title : Reservoir screening and feasibility assessment for underground natural gas storage in upper Assam basin
Ashutosh Ranjan, Oil India Limited, India
Title : Proportional application technologies for urea removal in aqueous waste effluents, and recommended technologies for varies industrial waste water.
Hossam Ahmed Aly Moustafa Teama, Abu Qir Fertilizers And Chemical Industries Company, Egypt
Title : Enhancing refinery operational excellence: An AI-powered approach to digital operations management
Ganesh Markad, Proclink Consulting, India