A few years ago, TotalEngergies saw an increase in mechanical failures in critical equipment for power generation, water injection, and gas compression in its growing fleet. Of the 105 breakdowns recorded, the team at TotalEnergies discovered that half of them could have been avoided with proper remote monitoring and early warning detection. To mitigate and prevent such losses, the company began monitoring downstream activities remotely and employed advanced analytics to anticipate equipment failures. The company saved €1.5M and 64 days of downtime in just one year.
“By using predictive analytics, we are able to perform early detection, which leads us to predict issues and anticipate failures.”
—Guillaume Da Costa, Remote Monitoring Team, TotalEnergies
TotalEnergies is diversifying its energy production to include renewables and moving toward net-zero emissions. To achieve this goal, it needed to precisely measure its current emissions. It used AVEVA™ PI System™ to collect data across all its operations and made this new pool of data available to its teams. With this emissions data, TotalEnergies has made significant gains in operational efficiency and streamlined monitoring emissions of all its assets.
Challenges
- Different teams using different calculations and visualizations of data resulted in duplication and inconsistencies
- Desire to operate more efficiently and track and mitigate greenhouse gas emissions, using a cost-effective, scalable digital solution
- New energy sources require new approaches to monitoring and maintenance
Solution
- Created a robust data contextualization and governance platform using AVEVA™ PI System™ to break down business silos, increase maintenance efficiency, track emissions and find new operational solutions.
- Deployed AVEVA™ Predictive Analytics to get early warnings of equipment failures and implement a predictive maintenance strategy to avoid unplanned downtime.
- Used AVEVA PI System’s asset framework to verify and standardize data down to the molecular level and reliably track CO2 and SO2 refinery emissions.
Results
- A sustainable digital twin consisting of a robust data information platform with models, analytics and enterprise visualization.
- A centralized and standardized approach with models and templates built once and then applied to hundreds of equipment and assets of the same type, supplying real-time data calculations, analysis and KPIs
- In one use case, reduced CO2 emissions by 15% annually
- Avoided a possible production shortfall of half a day of yearly upstream production in 2022, almost 500,000 barrels saved, as well as prevented catastrophic equipment failure
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