Data & Innovation • Oil & Gas • AWS
Artificial lift system design with a Generative AI assistant
In collaboration with Nubiral, an energy company implemented an AI solution to automate and optimize a key engineering process, enhancing the experience of its experts.
About the client
The leading private integrated energy company in Argentina and one of the most important in the region. With operations in oil and gas exploration and production, downstream, midstream, and renewable energy, it plays a key role in the energy development of Argentina, Bolivia, Brazil, Mexico, Paraguay, and Uruguay.
Its commitment to innovation and operational efficiency is a cornerstone of its leadership strategy, constantly seeking process optimization to maintain competitiveness and sustainability in a constantly evolving sector.
Needs
The design of Artificial Lift Systems (ALS) is a fundamental task in the Oil & Gas industry, requiring deep technical expertise and a significant investment of time. The client faced the challenge of optimizing this process, which traditionally relied on the individual knowledge of engineers and presented multiple interconnected challenges.
The vast number of variables across thousands of wells created high complexity and variability in designs, making it difficult to standardize an “optimal design.” This, in turn, fragmented critical knowledge among different specialists, posing both a risk and an obstacle to scaling best practices across the organization. As a result, the process was manual and lengthy, consuming valuable time that could have been reinvested in more strategic activities.
Adding to the complexity was a disconnect with inventory: designs did not always consider real-time stock availability, which could cause delays in the supply chain and project execution.
Solution
To overcome these challenges, Nubiral partnered with Pan American Energy to co-create a Generative AI Design Assistant. More than simple automation, the solution acts as a “Copilot” for engineers, combining artificial intelligence with human expertise to achieve superior results.
The solution, based on a multi-agent AI architecture, is capable of:
Automating the entire design workflow, from interpreting initial well conditions to generating a final auditable report.
Simulating and evaluating thousands of possible scenarios to identify the most efficient technical-economic design.
Integrating inventory variables, checking equipment and material availability to propose realistic, executable solutions.
Centralizing and consistently applying engineering best practices in every design, democratizing expert knowledge.
Success case • Oil & Gas
Relive the case presentation at the AWS Energy Symposium 2025 – Buenos Aires
Results
Optimized design time
Drastic reduction in hours dedicated to manual tasks, allowing engineers to focus on validation and more complex challenges.
Standardization and design quality
Ensures the consistent application of engineering best practices, elevating the quality and reliability of every proposed design.
Democratization of expert knowledge
The solution captures and applies the expertise of top specialists, making it accessible across the organization and supporting the development of new talent.
Supply chain synchronization
By considering available inventory, delays in material acquisition are minimized and project execution in the field is accelerated.
