Artificial Intelligence (AI) adoption has spread rapidly over the past few years, now spanning strategic and complex industries such as oil and gas. Petrobras stands as a prime example as this adoption is reshaping operational processes, management, and innovation. The purpose of this article is to analyze recent studies and initiatives in order to grasp the real use cases of AI and highlight the skills that professionals need to be a part of this change.
AI Applications at Petrobras
Several practical cases illustrate the depth of AI integration within the company:
Smart Torch: AI-enhanced software that utilizes cameras and operating metrics to monitor refinery flares during burning. It controls steam flow during flares to conserve fuel while ensuring the flares are burning off unburnt gases, thereby burning emissions at lower energy consumption and emissions rates during gas burning.
Petronemo: An AI generative assistant developed in partnership with Deloitte, aims to speed up maintenance recommendations in platforms and refineries. Unlike the past, when analysis took weeks, it now takes minutes, and by 2029, it is set to save R$20 million.
Revenue Forecasting with Machine Learning: This advanced system offers a daily prediction of the sales of oil, gas, and fuels with improved accuracy to the extent of reducing weekly forecast errors by as much as R$400 million. This, in effect, supports the company in managing cash flow and financial applications effectively.
Lê-AI: This system is designed to analyze various types of documents and records (including PDFs, images, and even handwritten notes). It enhances the identification of concealed assets and significantly speeds up the processes related to asset recovery and compliance investigations, potentially by as much as 90%.
Generative AI Agents Platform: The goal of this project is to implement smart agents that can watch sensors—thousands on each offshore platform—to identify failures or critical events beforehand. Because of the ongoing infancy of generative agent technology, the development remains cautious.
Observed Benefits
These initiatives highlight significant advantages:
Efficiency and sustainability: Accurate control of key processes such as gas flaring and predictive maintenance; substantial cost reduction and improved environmental compliance.
Faster and more accurate decision-making: Forecasting models and inspection tools increase accuracy while reducing waste and operational risks.
Intelligent automation: Tools like Lê-AI and Petronemo free human resources from repetitive tasks, enabling greater focus on strategic initiatives.
Challenges Faced
Despite progress, there are ongoing challenges:
Technical and cultural complexity: Implementing AI in critical environments requires caution, mindset change, and strong data governance.
Immaturity of generative agents: Operational and security risks prevent immediate large-scale adoption in mission-critical contexts.
Data quality and integration: Predictive models only work with clean, complete, and structured datasets—a challenge in large-scale legacy systems.
Essential Professional Skills
To thrive in this context, professionals must develop expertise in three key areas:
Technical: Machine learning, computer vision, NLP (as in Lê-AI), generative AI, data analysis, and predictive modeling for industrial platforms.
Operational: Knowledge of oil and gas infrastructure, refining, offshore operations; understanding of safety and compliance in critical environments.
Innovation and collaboration: Ability to work with multidisciplinary teams (engineers, data scientists, governance specialists), fostering disruptive and efficient solutions.
Strategic Recommendations
Invest in internal training: courses, partnerships with universities, and pilot projects with shared expertise.
Develop AI governance frameworks: committees, quality standards, auditability, and risk-mitigation plans.
Promote a culture of incremental innovation, where controlled testing evolves gradually into real-world deployment, as seen with Petrobras’ generative AI initiatives.
Conclusion
AI applications at Petrobras are rapidly expanding, delivering major advances in efficiency, sustainability, and decision-making. However, this transformation depends not only on technology but also on organizational culture and governance. Skilled, multidisciplinary professionals will be crucial in ensuring that AI generates long-term positive impacts in the oil and gas industry.
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