Integrity Risk Indicators (IRIs)

Maximizing petroleum profitability requires continuous optimization of operating conditions - such as adjusting a distillation column’s temperature profile to process heavier, more acidic crude blends. However, even minor operational changes can rapidly shift a system into an elevated corrosion regime, potentially accelerating asset degradation, reducing equipment reliability, and exposing facilities to unplanned outages. Hence, the economic value of process optimization is inseparable from the ability to understand and manage its associated integrity risks. This chapter presents Integrity Risk Indicators (IRIs), which utilize a fast, Bayesian RBI-style simulation engine to enable rapid screening of failure likelihood and sensitivity analyses within minutes. This allows process and integrity teams to evaluate operational scenarios before implementation. These indicators therefore serve as a tool that bridges the gap between daily risk awareness and the formal, consequence-based modelling required for full RBI studies.

Background

The Challenge: Moving Beyond Linear Estimates

In a live operating environment, plant profitability requires constant, real-time adjustments to changing market dynamics and feedstock variables. However, under these conditions, a traditional concept of linear corrosion tracking often fails to capture rapid transition to accelerated damage. As wall thickness decreases, risk does not grow steadily - it can escalate nonlinearly and, in some cases, accelerate exponentially. Small changes in parameters such as Total Acid Number (TAN), temperature, or velocity can shift steels and alloys from a safe operating window into a severe-corrosion regime.

The Solution: The Integrity Risk Indicator (IRI-Corrology®)

The Integrity Risk Indicators (IRIs) provide fast, API 581-inspired Bayesian likelihood-style screening for decision support, sensitivity analysis, and inspection/integrity advice. The framework integrates corrosion severity, uncertainty, and detection effectiveness to assess how operational changes, inspection strategies, and uncertainty assumptions affect the calculated score.

The Advantage

Access a high-speed, agile modeling engine designed for rapid, defensible engineering decisions:

Nonlinear Likelihood Scaling: Uses a logP_f-based Bayesian transformation to capture nonlinear increases in failure likelihood as components approach critical structural integrity thresholds.
Inspection Credit Visibility: Shows how inspection effectiveness (Class A-E) influences uncertainty and susceptibility scores.
Automated Prior Confidence Assignment: Prior confidence is automatically derived from the uninspected time interval (t_uncertainty) using predefined thresholds. Manual override is available when engineering judgment or additional context requires a different confidence level.
Structural Floor Control: Supports Manual t_min overrides when justified by engineering basis.
API 581-aligned Score Classification: Maps final score status using logP_f into Very Low, Low, Moderate, and Critical score bands.
Engineering Advisory Output: Supports targeted mitigation planning across process controls, inspection strategy, and data-quality improvements.

Methodology & Risk Guidance

This Integrity Risk Indicator (IRI) uses a margin-aware, API 581-inspired model for engineering decision support and degradation trend analysis. Unlike full API 581 risk-based models, this tool computes a synthetic failure-likelihood-style indicator (P_f proxy) derived from model corrosion rate inputs and measured wall-thickness context.

The reported score is therefore a failure-likelihood / integrity indicator, not a complete API RBI risk result based on likelihood × consequence. Consequence-of-failure is outside the scope of this score.

When an optional cracking path is enabled (Amine Cracking, Polythionic Acid SCC, Wet H₂S Cracking), the tool evaluates cracking as a separate damage mechanism. If both thinning DF and cracking DF are active, the model combines them through an API 581-style multi-damage-mechanism DF path before Pf/logPf/score are derived. If observed cracks remain unresolved, the assessment enters an FFS-required disposition and FFS is required before normal assessment can continue. The operator-facing score and remaining service-life presentation are governed by the applicable mechanism-specific IRI workflow until that FFS condition is resolved.

Predictive Mode: the score is calculated from the model corrosion rate using the current inspection/thickness context and the target simulation horizon. Prior confidence uses the common bands <3 years → High, 3–5 years → Medium, ≥5 years → Low where the applicable IRI workflow derives confidence from an inspection-anchored interval.
Inspection Mode: the calculation utilises an effective corrosion rate (CR__eff) selected from long-term thickness loss (CR_LT), short-term thickness loss (CR_ST), or model corrosion rate fallback, depending on data validity and the conservative-rate setting. The baseline remains model-anchored for comparison consistency. Prior confidence is auto-derived from the applicable inspection-data or evidence interval using the common thresholds.

Score Scaling

The scoring engine applies a logarithmic-exponential transformation so that the calculated score escalates as wall thickness approaches the structural minimum threshold (t_min). This represents nonlinear escalation in late-life degradation states while maintaining bounded growth to avoid runaway numerical behavior. Final score bands are reported across four distinct tiers: Very Low, Low, Moderate, and Critical.

Uncertainty / Confidence Handling

The model combines: (1) prior confidence, (2) inspection-effectiveness conditional probabilities, and (3) a Bayesian 3-state damage weighting with a minimum weight floor for stability in edge cases. Lower inspection effectiveness (for example Class E versus Class A) increases conservatism, while online monitoring applies a score credit when enabled in the common score calculation.

The prior confidence is automatically derived using inspection or evidence interval. Confidence level may be manually overridden by the user when engineering judgment or additional context requires a different confidence level.

Scope and Governance

This framework is designed for rapid sensitivity analysis and likelihood-style score screening across multiple damage mechanisms and the materials supported by each individual IRI. Outputs support engineering decisions but do not replace formal FFS per API 579, or site-governed RBI assessments.

Integrity Risk Indicators (IRIs)

This section provides a list of IRIs suitable for managing corrosion susceptibility in various refining and petrochemical processes.

Access is available only with a Advanced subscription level or higher.