Probabilistic Risk leveraging
C-FER Risk Models

The C-FER risk models use probabilistic methods to estimate quantitative risk levels for all major threats to pipeline integrity. The risk models have evolved and improved through 25 years of use and validation by oil and gas pipeline operators world-wide.

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C-FER is widely considered to be a leading industry expert regarding pipeline risk assessment. The embedded quantitative C-FER models within Cognitive Integrity Management (“CIM”), allow pipeline operators to have real-time capabilities for assessing risk on a rolling mile for each pipeline segment while at the same time being able to monitor the entire pipeline system through CIM’s embedded Microsoft PowerBI dashboards. Operator based targeted reliability thresholds are assigned industry equivalent rupture rates tiered by quadrant, so operators know how they are doing compared to their peers.

C-FER risk models consider the nine threat categories described in ASME B31.8S. The models use two techniques to estimate the probability of failure for each threat:

  • Historical-based approaches use industry failure rates and attribute-based adjustment factors to derive failure probabilities. Threats assessed using this approach include Construction Defects, Equipment Failures, Incorrect Operation and Maintenance, and Seismic Hazards.
  • Structural Reliability approaches leverage pipeline condition data within engineering assessment models to produce failure probabilities. Threats assessed using this approach include: Manufacturing Defects, Equipment Impact, External Corrosion, Internal Corrosion and Stress Corrosion Cracking.

The probability of failure (“PoF”) is rolled up across all nine threats as it is calculated down the line for each segment to determine the PoF for a small leak, large leak, or rupture (big leak). Each system is then rolled up across the entire pipeline system.

How you can leverage a probabilistic risk assessment:

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CIM Data Management

Any risk model is only as good as the data it is provided. This is especially critical for quantitative risk models. The CIM platform ensures data is valid through its ingestion and alignment algorithms, interacting threat classification, and hundreds of data validation checks. In addition, the CIM CGR for corrosion is used as input for both the external and internal corrosion models, thus ensuring the most accurate Probability of Failure estimate possible for these threats.

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Probability of Failure

As opposed to qualitative probability models which rely heavily on SME input to assign index weighting, C-FER risk models estimate probability of failure directly on pipeline condition data. The models estimate the likelihood of small leaks, large leaks, and ruptures by quantifying the uncertainty associated with these low probability events. The results are presented on a rolling mile basis as a standard dashboard in CIM within the normal business process flow, so your teams have the whole picture.

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Consequential of Failure

Today CIM utilizes a quadrantile ranking that is based on PHMSA derived equivalent rupture rates per industry reported data. It factors in both the operators targeted reliability threshold and is calculated to account for HCA and drain down while quantifying the reliability profile along the pipeline.

*Additional reliability thresholds such as the C-FER Annex O (Z662) and the PRCI threshold are being evaluated for inclusion into CIM at some point in the future.

Business Value:

Incorporate real-time quantitative PoF into your standard IMP process by identifying anomalies that exceed your targeted reliability thresholds, drill-down to view previous excavations and as found results and let CIM suggest the ideal ROI between tool runs and digs.

No more importing, exporting, and manipulating data across siloed systems, the entire business process is now centralized in a modern enterprise-wide SaaS solution based on some of the most industry validated risk models by C-FER.

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Join your peers.

Elevate your risk program by quantifying it based on data with the incorporation of probabilistic risk assessments available in real-time.

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