Heavy Industry AI That Shows Its Work
The ”Added Context” series distills Bolo Founder and CEO Diti Sood’s perspectives on AI, operations, and the future of heavy industry from articles published in Medium. This installment draws from “Agents Don’t Replace Physics. They Make It Scale in Oil & Gas.”
Oil and gas engineers use production data, reservoir conditions, injection history, completion records, and test results to evaluate well performance. Bolo organizes this evidence within a traceable engineering workflow, helping teams focus on the production decisions that matter most.
Bolo's core analysis—engineering logic, decline analysis, water-oil ratio diagnostics, injectivity screens, voidage replacement ratio, and pattern support—is deterministic and auditable. AI comes in to handle the fragmented records around that core: searching, normalizing units and naming conventions, and flagging what's missing, without ever outrunning what the data supports.
That split in responsibilities exists because production and reservoir engineers already know how to synthesize this evidence. What they lack is time, since that evidence sits scattered across a number of incomplete systems for every well.
Bolo is designed to gather scattered information and put it in front of the right person at the right time, leaving the judgment and subsequent action to the engineer.
A waterflood surveillance program with an independent operator highlighted that lesson in two instances. On one well, the real cause of underperformance was a deliberate test the operator had run, which was knowledge that existed only in an engineer's memory; it never made it into any database AI could pull from. On a separate injector well with a long mechanical history, the issue wasn't missing knowledge but buried knowledge: two datasets telling conflicting stories about where the well was actually injecting water, sitting in documents that had never been checked side by side.
AI wasn’t the decision-maker in these instances; it merely flagged anomalies that were critical for an engineer to be aware of. And that’s exactly how it was built to work. It used the context it had to draw attention to an issue that needed human expertise and action.
Honesty builds trust—especially from technology usually programmed to sound confident even when it’s wrong. With Bolo, every result shows engineers how strongly the available evidence supports the finding.
Results all carry a confidence tier. Findings with high confidence are reported with direct language. With low confidence, Bolo names uncertainty plainly, rather than generating an answer the data doesn't support.
Bolo also checks whether a historical test applies to the well’s current completion before incorporating it into a present-day analysis. This interval-match check helps engineers evaluate the relevance of prior results and focus follow-up on the inputs most likely to affect the decision.
Being able to trust the output means being able to see its limits. Engineers can move quickly on high-confidence findings, investigate lower-confidence results with the right degree of caution, and understand exactly which evidence needs further validation before acting.
Learn how deterministic logic, systematic evidence assembly, and confidence-aware outputs help oil and gas teams scale rigorous engineering analysis across more wells and workflows.