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-aligned Bayesian likelihood screening for decision support, sensitivity analysis, and inspection/integrity advices. The framework integrates corrosion severity, uncertainty, and detection effectiveness to assess how operational changes, inspection strategies, and uncertainty assumptions affect integrity risk.

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 Likelihood Classification: Maps final likelihood status using logP_f into Very Low, Low, Moderate, and Critical likelihood 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 Likelihood of Failure (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 the optional Amine Cracking path is enabled, the tool evaluates cracking as a separate damage mechanism. If both thinning DF and cracking DF are active, the backend combines them through an API 581-style multi-damage-mechanism DF path before Pf/logPf/score are derived. If cracks are observed and remain unattended or unrepaired, the case is treated as an FFS-gated hard stop: Amine Cracking Df evaluation is stopped, the result is assigned a Critical disposition, and FFS is required before the assessment can continue. The operator-facing result intentionally suppresses the numeric susceptibility score and remaining service life and displays the red message, “Risk is not calculated because cracking has been detected and FFS is required before the asset can be returned to service.” A normal thinning-only score and service-life display are used only after that FFS condition is resolved. This amine-cracking hard-stop path applies only to applicable Carbon Steel cases; it is not applied to stainless materials.

Predictive Mode: likelihood intensity is calculated from the model corrosion rate projected over the total simulated exposure window (t_total = Target Simulation Year − Start of Service Year). Prior confidence is automatically derived from the uninspected interval (t_uncertainty = Target Simulation Year − Last Inspection Year): <3 years → High, 3–5 years → Medium, ≥5 years → Low, preserving API 581 conservative safety margins.
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 likewise auto-derived from t_uncertainty using the same thresholds.

Susceptibility Scaling

The scoring engine applies a logarithmic-exponential transformation so that probability escalation increases as wall thickness approaches the structural minimum threshold (t_min). This reflects the physical reality of failure likelihood escalation in late-life degradation states while maintaining bounded growth to avoid runaway numerical behavior. Final status bands are API 581-aligned and reported across four distinct tiers: Very Low, Low Likelihood, 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 risk credit (monitoring factor = 0.85) when enabled.

In both modes, prior confidence is automatically derived from the uninspected time interval (t_uncertainty) in the standard UI workflow. 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 of failure screening across multiple damage mechanisms and materials (including broad CRA selections). Outputs support engineering decisions but do not replace formal FFS, 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.