Faced with increasing pressure to reduce non-productive time (NPT) and ensure safety in complex operational environments, Petrobras recognized the need to innovate in its monitoring methodologies. Reactive conventional tools and fragmented data required a solution that went beyond traditional analytics to anticipate downhole issues.
The answer was the adoption of a simulation-based drilling digital twin, which has become a fundamental pillar, redefining the engineering workflow from reactive maintenance to predictive decision-making.
By using a hybrid solution that anchors machine learning (LSTM) in rigorous physics-based models, the partnership not only expanded its know-how in detecting anomalies like washouts and casing wear but also achieved tangible results. This includes the optimization of well delivery, which generated significant cascading savings of US$ 333 million, and the continuous improvement of sustainability metrics across a 10-year journey.
Challenge
Petrobras needed a proactive, data-driven approach to anticipate downhole issues and reduce non-productive time (NPT) in high-stakes drilling operations, where conventional tools lacked predictive capability.
Solution
The partnership implemented a simulation-based drilling digital twin, anchored in rigorous physics models and machine learning. Operated by a 24/7 expert team, the solution was integrated into the decision-making flow to validate data accuracy in real time.
Key results
The initiative resulted in US$ 333 million in cost savings as a result of 488 rig days saved across 600+ wells. The project not only optimized costs but also accelerated well delivery and advanced sustainability goals, significantly reducing the carbon footprint per barrel.