I remember my first day on a workover rig—fresh out of school, watching a team pull tubing from an old well. I didn’t know much yet, but I knew I wanted to be part of it. From that moment on, petroleum engineering wasn’t just a job. It became my purpose.
Over the years, my career has taken me from the fields of Latin America to corporate offices in Houston, from reservoir characterization and production optimization to coordinating technical and commercial proposals and global tenders worth billions. One lesson has stayed with me: The upstream sector evolves when we match technical depth with bold innovation. And few areas need that more urgently than our mature oil fields.
It’s time we treat them like the strategic assets they are.
For decades, we’ve talked about modernizing oil and gas but too often, the focus has been on greenfield technologies, new frontiers, and flashy concepts. Meanwhile, many of the fields that built this industry, our mature, brownfield assets, have been running on legacy systems, reactive workflows, and intuition-driven decisions.
These fields still matter. In the US, they represent a significant share of daily production. But aging infrastructure, equipment failures, rising OPEX, and fractured data systems are holding them back. In a low-margin, high-expectation environment, this is no longer sustainable.
That’s where artificial intelligence (AI) is beginning to change the narrative.
AI is beginning to transform how we manage these wells, with measurable results. Machine learning models are now helping engineers predict electrical submersible pump failures before they happen, optimize drawdown more efficiently, and generate reliable forecasts even when data is scarce or noisy. We’re moving from reactive decisions to proactive strategies.
The real shift, though, isn’t just technological—it’s cultural. AI encourages us to rethink legacy workflows: from manual surveillance to smart automation, from isolated systems to integrated insight.
I've had the privilege to witness this transformation firsthand, supporting multidisciplinary teams, coordinating technical and commercial bids, and helping bring digital strategies into mature upstream assets. The results are tangible: reduced downtime, improved uptime, better planning, and more confident decisions.
Of course, AI isn’t a plug-and-play solution. It requires clean data, domain knowledge, and trust in the models. It demands that engineers stay curious, adaptable, and willing to engage with digital tools that may challenge traditional workflows.
But when done right, the impact is clear.
- Downtime goes down.
- Uptime improves.
- Planning becomes sharper.
- Field life is extended.
So, what can we do now as an industry?
Three ways to lead this shift:
1. Invest in data integrity. AI is only as strong as the information it learns from. Clean, structured data from aging fields is a long-term asset.
2. Build cross-disciplinary teams. Field knowledge and data science should not operate in silos. Bring engineers and analysts into the same conversation early.
3. Start small, scale smart. Begin with a few wells or pads to test results, learn what works, and build support before expanding across the field.
At a time when the industry is being asked to produce more with less, reduce emissions, and deliver shareholder value, AI-enhanced operations in mature fields offer one of the most immediate and scalable opportunities for impact.
And perhaps most importantly, it allows us, as petroleum engineers, to continue doing what we do best: solving complex problems with practical, forward-thinking solutions.
Because this isn’t just a job. For many of us, it’s a calling. And it’s time to bring that passion into the next chapter—powered by data, guided by experience, and driven by purpose.
