Integrity governance for linear assets — ILI anomaly growth analysis, cathodic protection compliance, and GIS-driven risk topography for hundreds of miles of pipe across multiple jurisdictions.
Pipeline integrity engineers, GIS specialists, and midstream operations directors responsible for long-distance linear assets under PHMSA 49 CFR 192/195, CER, or equivalent regulators. Built for organizations that ingest smart-pig vendor files, run multi-year repair-prioritization programs, and need defensible records for every dig decision.
Ingest MFL and UT vendor files from multiple tool vendors. The growth engine matches anomalies across runs (e.g. 2021 vs 2026) and calculates depth and length growth rates per milepost. Retirement-date projections per anomaly feed directly into the repair priority queue.
Test point readings are tracked against the -850 mV protection criterion (or your facility-specific threshold). Segments that drop into unprotected state trigger governance events and CP technician dispatch. Long-term trend analysis identifies segments with degrading coating performance.
Overlay pipeline hierarchy, anomaly density, soil corrosivity, population density, and HCA boundaries on a single map. Heat-map visualization surfaces the spatial concentration of risk — the segments your next dig program should target first.
Every dig recommendation links back to the anomaly records, growth-rate calculation, and risk topography that justified it. Regulator audit packages include the complete decision chain — no manual reconstruction.
Move from calendar-driven smart-pig runs and reactive digs to a multi-year, risk-ranked integrity program. Pipeline operators using this suite typically defer 20–40% of historically scheduled digs (because the data shows no actionable growth) while accelerating digs on segments where growth rates exceed safe thresholds. Compliance evidence for PHMSA, CER, and insurer audits is generated automatically as the program runs.
We will configure ILI growth analysis using a sample of your tool run data.