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Machine Learning & AI For Upstream Onshore Oil & Gas 2019

Machine Learning & AI For Upstream Onshore Oil & Gas 2019

28 August 2019 - 29 August 2019


 

Digital transformations are predicted to transform the economics of upstream operations by reducing expenditures, improving maintenance efficiency, and providing a granular view of workflows, enabling more effective decision-making. At the heart of all these digitisation efforts lies machine learning.

Machine learning and AI applications could save the oil and gas industry as much as US$50 billion in the coming decade, according to McKinsey. Since the global oil price re-set in late 2014, companies have increasingly been looking at technology to reduce costs, improve efficiency and minimise downtime but there is still a lack of understanding of what value AI can actually create for the industry, and what cost and operational benefits it can bring.

 

DESIGNED TO DELIVER SOLUTIONS FOR UPSTREAM ONSHORE OPERATORS

 

Operators, large, medium or small, are continually looking for ways to improve operational efficiency, make operations faster and more efficient, make assets run better, find bottlenecks in processes, find asset failures before they occur, eliminate unplanned downtime. What they are beginning to realise is that there are ways to improve every single one of those metrics using Machine Learning and AI.

The recent influx of AI technologies means the opportunity to process numerous real-time data sets, every minute of every day, and build models where you are able to quantity change and achieve even greater cost savings in the short term, all operational areas, is now within reach. Production optimisation is definitely where the real advantage is to solve engineering problems with Machine Learning and AI.

With this mind, the Machine Learning & AI For Upstream Onshore Oil & Gas 2019 purely focuses on understanding the profitable applications of Machine Learning and AI, primarily for optimising production for onshore E&Ps, and examine how to improve operational efficiencies in drilling and completions.