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.
Selim Sanad Shaker
Geopressure Analysis Services, United States
John W Sheffield
Purdue University, United States
Michael Radicone
Heat Transfer Research, Inc, United States
William Gothard
Ahmic Aerospace, United States
Guido Gabriel Piccone
ADNOC Onshore, United Arab Emirates
Andrey Yugay
ADNOC Onshore, United Arab Emirates
Pranay Asthana
Saudi Aramco, Saudi ArabiaSubmit your abstract Today
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Title : Mature field evaluation and redevelopment case histories and lessons
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Nur Hassan, Central Queensland University, Australia
Title : Advancements in vacuum insulated technologies for energy efficiency and sustainable temperature sensitive logistics
Saim Memon, Sanyou London Pvt Ltd, United Kingdom
Title : Salt basins exploration risks: The good, bad and ugly
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Title : Unlocking GHG reduction potential and enhanced oil recovery with direct contact steam generation technology: A case study in Lloydminster, Canada
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