Pipeline Integrity
How to Build a Susceptibility Model for a Threat Your Inspection Tools Can't Fully See
A practical framework for turning weak, individually unreliable signals into a single predictive susceptibility score, using bottom-side internal...
Beyond Pit-to-Pit: What 12 Years of ILI Data Taught Us About Corrosion Growth Rates
On a 10-inch line that's been in the ground since 1971, a standard pit-to-pit corrosion growth model predicted a metal loss anomaly would be at 50%...
Integrating Geospatial Intelligence for Proactive Geohazard Management
Interested in joining a Private Preview for Geohazards? Contact Us. Understanding the interaction between pipelines and their surrounding environment...
Using Machine Learning to Manage Data Gaps and Improve Decision-Making
Machine learning is often known for building prediction models using training data, i.e., identifying your susceptibility to an internal corrosion...
Enterprise External Corrosion Management: Reducing Variability, Increasing Defensibility
Why consistent interpretation — not necessarily more data — determines program maturity. External corrosion surveys generate large volumes of...
Deploying Probabilistic Corrosion Forecasting at Scale: What Unified Data Makes Possible
Integrity analytics doesn't scale without a unified inspection history. To demonstrate what becomes possible once inspection data is aligned,...
From Unified Data to Scalable Automation
Pipeline integrity teams face an increasingly difficult challenge: growing system complexity and tighter regulatory timelines, with the same or fewer...
