Sustainability doesn’t have to come at the expense of profitability. By amplifying the potential of your oil and gas data with AI-powered analytics and predictive insights, the case is just the opposite: More reliable, efficient operations lead to more profitable, sustainable results. Discover how leaders in the oil and gas industry are building on the capabilities of AVEVA PI System to extend equipment life, reduce emissions, and ensure production levels and safety at the same time.
Learn how data can help you reach your goals
Sustainability
Support your sustainability efforts and reduce emissions by breaking down data silos and bridging the divide between disconnected systems.
- How oil companies can lead the energy transition:
Discover how oil and gas companies can achieve their decarbonization goals by embracing renewable energy, minimizing risk, and investing in sustainable business models.
- Decarbonizing the oil and gas industry with integrated engineering, operational, and carbon data management and layers of analytics:
Consider the eight dimensions of decarbonization as you develop your sustainability strategy.
Reliability
Take a proactive, data-centric approach to operations and maintenance by combining AVEVA PI System with the power of AI and machine learning.
- Next-level reliability: Power your AVEVA PI System with AI and machine learning:
Extend your AVEVA PI System with purpose-built AI and machine learning for increased reliability and profitability.
- Maximize asset reliability and performance with AI:
Take your analytics strategy to the next lever to drive faster, more sustainable results.
Learn how AVEVA customers in the oil and gas industry are achieving their goals
Shell
Shell used AVEVA PI System to monitor subsea blowout preventers, resulting in over $5M in cost avoidance savings.
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Petronas
Petronas combined AVEVA™ Predictive Analytics with AVEVA PI System to minimize unplanned downtime and save $33M.
Saudi Aramco
Saudi Aramco leverages AVEVA Predictive Analytics, AVEVA PI System, and process simulation to improve asset management for 2,500 pieces of critical rotating equipment
