Oil & Gas & Petrochemical Facilities

Moisture Risk Intelligence for CUI — Decision-Ready, Non-Intrusive, RBI-Aligned

Continuously quantify moisture persistence and wet–dry cycling analytics under insulation — and convert it into auditable Risk-Based Inspection (RBI) evidence for inspection prioritisation and defensible deferral.

Request a CUI Pilot Scope (2 weeks) Download RBI Appendix (PDF)

Non-intrusive deployment • Persistence + cycling analytics • RBI-mapped outputs

✔ Non-intrusive deployment — no routine insulation removal or penetration

✔ Persistence + cycling analytics — not snapshot “wet/dry” alarms

✔ RBI-mapped outputs — PoF influence and defensible deferral

CUI Isn’t a “Wet or Dry” Problem. It’s a Persistence + Cycling Problem.

In downstream refineries and offshore facilities, concealed condensation and repeated wet–dry cycling often drives corrosion risk long before metal loss is externally detectable.

Traditional inspection regimes are periodic, labour-intensive, and disruptive, and frequently fail to identify early-stage exposure behaviour that escalates Probability of Failure (PoF).

Over-inspection and insulation removal (high cost, high disruption), or
Limited visibility between inspections, risking undetected degradation

Why It Matters

Hidden moisture within insulated systems directly influences corrosion mechanisms, degradation rates, and inspection confidence — yet remains largely unmeasured between inspection campaigns.

In tropical climates, behaviour-based moisture evidence matters more than one-time readings.

Early escalation of Probability of Failure (PoF)
Uncertainty in inspection interval justification
Increased unplanned insulation removal
Conservative inspection scopes driven by lack of evidence

From Concealed Moisture → to Inspection Decisions

Moisture Persistence Function

Quantifies continuity, recurrence, and distribution of moisture exposure over defined evidence windows.

Wet–Dry Cycling Severity Index

Measures exposure stress based on transition frequency, dwell time, and clustering of wet–dry events.

RBI-Ready Evidence Outputs

Risk rankings, inspection targeting, and defensible inspection deferral packs aligned to RBI workflows.

How It Works — Four Simple Steps

Place
Deploy non-intrusive monitoring nodes at moisture-critical zones without disturbing insulation systems.
Collect
Continuously acquire time-series moisture and environmental data via IIoT.
Analyse
Apply persistence and cycling analytics across defined evidence windows.
Decide
Generate auditable RBI insights: zone risk ranking, inspection target lists, and deferral justification.

Where We Monitor

Typical CUI-Critical Zones

Low points and dead legs
Pipe supports, clamps, and shoes
Nozzles, penetrations, and interfaces
Terminations and damaged jacketing
High-condensation interfaces and water traps

👉 Share your insulation specification and operating temperature bands — receive a tailored placement plan.

Operational Outcomes

Earlier detection of chronic moisture exposure pathways
Improved inspection targeting based on measured PoF uplift drivers
Reduced unnecessary insulation removal and over-inspection
Stronger justification for inspection deferral decisions
Clear, auditable evidence for RBI, TA, and MOC documentation

What You Get — Pilot Deliverables

CUI moisture risk heatmap by circuit or zone
Evidence-window report suitable for TA / MOC / RBI records
Inspection target shortlist (highest PoF uplift contributors)
Inspection deferral justification blocks (where supported)
Data export for integrity and inspection teams

Why This Beats “Sensor-Only” Monitoring

Not just detecting wetness — quantifying exposure behaviour
Not just corrosion — leading indicators before damage manifests
Not just dashboards — inspection-grade, auditable RBI outputs

FAQs

Does this replace inspection?
No. It prioritises where and when to inspect — and supports defensible deferral where evidence exists.

Is it intrusive?
Designed to minimise operational disruption and insulation disturbance.

Does it work in tropical humidity?
Yes. Analytics focus on persistence and cycling — not fragile humidity thresholds.