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 and sensitivity analyses within minutes - allowing 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 accelerates exponentially. Small changes in parameters such as Total Acid Number (TAN), temperature, or velocity can shift steels and alloys from a stable operating window into a severe-corrosion regime.

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

The IRIs provide fast, API 581-aligned Bayesian risk screening for decision support, sensitivity analysis, and risk trending. The framework combines corrosion severity, uncertainty, and detection effectiveness so teams can quickly evaluate how operating changes, inspection strategy, and confidence assumptions influence risk.

The Advantage

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

  • Non-Linear Risk Scaling: Uses a logPf-based Bayesian transformation to reflect late-life risk acceleration near structural boundaries.

  • Inspection Credit Visibility: Shows how inspection effectiveness (Class A-E) changes uncertainty and risk score.

  • Predefined Prior Confidence: Prior confidence is predefined from years since the last inspection using t_uncertainty thresholds: less than 3 years = High, 3-5 years = Medium, 5 years or more = Low.

  • Structural Floor Control: Supports manual t_min overrides when justified by engineering basis.

  • API 581-Aligned Risk Bands: Final status is mapped by logPf into Very Low, Low Risk, Moderate, and Critical 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-aligned Bayesian framework for engineering screening and trend analysis. Unlike linear indicators, it computes a synthetic Probability of Failure proxy (Pf / logPf) from modeled corrosion behavior and, where available, measured wall-thickness evidence.

Important terminology note: within IRIs, the displayed “risk score” is a likelihood-of-failure proxy derived from API 581-aligned Bayesian PoF logic (Pf/log Pf pathway). It is not a full consequence-integrated RBI risk value. Final risk acceptance should combine this likelihood output with site-specific consequence, criticality, and RBI governance requirements.

  • Predictive Mode: Uses modeled corrosion behavior with simulation horizon inputs to project risk under future operating assumptions.
  • Inspection Mode: Uses measured thickness history to derive effective corrosion rate (CR_eff) evidence (including long-term/short-term routing logic where applicable), while preserving model anchoring for consistent comparison.

In both modes, prior confidence is predefined from the uninspected interval based on years since last inspection (t_uncertainty), using fixed thresholds for conservative consistency.

Risk Scaling

The engine maps Bayesian outputs through logPf into bounded risk scores and API 581-aligned status bands. This preserves sensitivity in low-to-mid risk regions while avoiding unstable behavior at high damage states.

Uncertainty / Confidence Handling

Risk score response is governed by:

  • predefined prior confidence from t_uncertainty,
  • inspection effectiveness conditional weighting (A-E), and
  • Bayesian multi-state damage weighting with stability floors.

Lower inspection effectiveness increases conservatism. Online monitoring, where enabled and applicable, applies monitoring credit within the Bayesian pathway.

For tools that include optional secondary damage mechanisms such as amine cracking, wet H2S cracking, or PTA SCC, the backend follows a simple rule: one active mechanism stays on the single-mechanism path, while two or more active mechanisms are combined at DF level using an API 581-style multi-damage-mechanism path before Pf/logPf/score are derived.

Scope and Governance

This framework is designed for rapid sensitivity analysis and risk 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 Professional + subscription level or higher.