From Bottleneck to Breakthrough: How AI is Accelerating MAOP and TVC Compliance

In the midstream oil and gas sector, pipeline integrity is non-negotiable—and ensuring data is traceable, verifiable, and complete (TVC) is no longer a “nice to have.” It’s mission-critical. With the PHMSA 2028 Maximum Allowable Operating Pressure (MAOP) verification deadline fast approaching and the stakes of non-compliance growing exponentially, owner-operators are searching for faster, smarter ways to manage the mountain of legacy documentation standing between them and compliance.

At Vintri Technologies, we’ve seen firsthand how artificial intelligence is transforming the way pipeline operators approach data integrity. But while AI may be the engine, it’s the right expertise—an “expert in the loop”—that drives value at scale.

Why Traditional Methods Are Falling Short

Manual review of pipeline records—material test reports (MTRs), weld logs, pressure test reports, process and instrumentation diagrams (P&IDs)—is slow, expensive, and prone to error. Even with optical character recognition (OCR) tools in play, the process often requires countless hours of spreadsheet manipulation, duplicate checks, and manual interpretation.

Take this real-world example: In 2023, one of our analysts spent over 100 hours data mining weld records for a major E&P operator using a hybrid manual/OCR approach. When we ran the exact same records through Vintri’s AI-driven workflow—with an expert in the loop—we achieved the same deliverable in just 13 minutes.

That’s a time savings of over 99%. And it’s not just faster—it’s better. The structured output was more complete, more consistent, and more defensible.

Expert in the Loop vs. Human in the Loop: What’s the Difference?

At Vintri, we don’t just put a “human in the loop.” We put an expert in the loop.

While “human in the loop” (HITL) models rely on any human input to improve AI accuracy, “expert in the loop” goes a step further by embedding qualified subject matter experts—individuals with deep knowledge of midstream operations, documentation, and regulatory nuances—directly into the training, QA, and exception handling layers of the process. These experts don’t just validate outputs; they bring essential context to the intricacies and complexity of midstream industry data, enabling more accurate, relevant, and trusted results.

This ensures every model output aligns with operational realities, not just statistical assumptions.

It’s this balance of machine speed and human precision that lets us build high-fidelity datasets quickly—turning unstructured documents into actionable intelligence without compromising quality.

Inside the Workflow: How Vintri’s AI Transforms Pipeline Records

Vintri’s AI-powered data integrity platform is purpose-built for the complexity of midstream records. We’ve developed a modular workflow that handles the full lifecycle of unstructured document processing:

• Document Classification: Our AI can distinguish among a wide range of formats and record types, including poorly scanned PDFs, spreadsheets, images, and handwritten notes.

• Automated Ingestion & Extraction: Using natural language processing (NLP) and computer vision, the platform pulls key attributes from even the most chaotic file types.

• Accessibility Checks: Every record is validated for completeness. Missing fields or unreadable values are flagged for review.

• Omissions & Discrepancy Detection: Our models cross-verify extracted data against related documents, highlighting mismatches or gaps that could compromise TVC compliance.

This isn’t generic AI. We custom-train neural networks using real-world data and industry-specific scenarios. In fact, it’s common for us to build and compose multiple models just to handle variations in documentation from the same supplier.

Built for Complexity. Proven in the Field

The midstream industry is anything but standardized. That’s why there is no one-size-fits-all AI.

Vintri’s approach is agile and responsive to each operator’s needs. By composing multiple domain-specific models and integrating expert oversight, we’ve helped clients reduce data extraction timelines by over 90% while increasing confidence in the final output.

Whether you're running MAOP validation on a decades-old pipeline or consolidating TVC records for a new construction project, we streamline the process with unmatched speed and integrity.

Collaboration is the Key to Better Models

As AI adoption increases across the sector, the industry faces a key opportunity: collaboration.

Better data models require better data. Through greater information sharing—especially around document structures, terminology, and supplier-specific formats—we can collectively improve model performance and push for broader standardization.

This not only reduces future data conditioning costs but also strengthens the industry’s overall resilience in the face of regulatory scrutiny.

What Could You Accomplish with 99.8% of Your Time Back?

Most operators don’t lack insight—they lack the time to get there. Data wrangling often consumes hundreds of hours before any meaningful analysis even begins.

What would your team do with that time back?

• Advance MAOP reconfirmation initiatives.

• Perform deeper engineering analysis.

• Launch predictive maintenance strategies.

• Reduce project risk and rework.

This is the promise of AI-driven data integrity. Not automation for automation’s sake, but a measurable shift in how you manage your most critical asset: information.

When “Nice to Have” Becomes “Must Have”

We’ve already seen how poor data traceability can lead to multimillion-dollar risks. One recent project Vintri reviewed was on the brink of $60 million in remediation due to incomplete and unverifiable records. These risks aren’t hypothetical—they’re happening now.

The message is clear: You don’t need AI someday. You need it now.

Let’s Talk

If unstructured records are slowing you down—or putting your compliance at risk—Vintri can help. We’ve built our platform specifically for midstream owner-operators like you.

Let’s work together to transform your documentation backlog into a Verified Single Source of Truth—at a fraction of the time and cost of traditional methods.

Click here to learn more and book a discussion with our team.