Introducing Risk Management for Pipelines: Quantifying Threats

In the high-stakes world of pipeline operations, risk isn’t just a compliance checkbox—it’s the fine line between proactive maintenance and pipeline failure. Yet for many operators, risk assessments are still mired in outdated, siloed systems, unreliable spreadsheets, and subjective guesswork.

Enter the Risk Management Module: a centralized, ML-enhanced platform that’s redefining how the industry quantifies pipeline threats. Built on proven best practice engineering models backed by incident data from PHMSA, this module provides a real-time probability of failure using real inspection data, with the ultimate goal of keeping infrastructure safe, efficient, and compliant.

Let’s dive into what makes this platform a game-changer.

From Gut Feel to Data-Driven: What Makes This Different?

Legacy systems have long relied on qualitative models that depend on SME opinions and infrequent updates. The result? Static, subjective snapshots of risk that often miss the mark. Worse still, they can take weeks to compile and still leave operators guessing where the next failure might occur.

The Risk Management Module flips that script.

It combines two powerful modeling approaches to calculate the Probability of Failure (PoF)*:

1. Structural Reliability-Based Models: These use inline inspection (ILI) data to evaluate threats like corrosion, stress corrosion cracking, and mechanical damage. By treating material properties and loads as probabilistic variables, these models provide precise PoF values—no guesswork required.
2. Historical Data Models: Using incident data from the PHMSA database, this approach calculates PoF for threats like equipment failure, incorrect operations, manufacturing defects, and even weather-related events. Think of it as the statistical reality check for your pipeline health.

These two models provide a technically sound understanding of your pipeline threat score, grounded in physics, informed by history, and updated as often as your data is. 

*Phase 1 of the Risk Management module, available now, calculates a threat (PoF) score. Phase 2 will integrate a consequence of failure (CoF) score. 

Centralized, Scalable, Smart

The Risk Management module isn't just a model but a platform built to integrate everything from ILI and GIS data to internal corrosion control activities and external corrosion control surveys to incident and repair reports. Hosted on Microsoft Azure, the system easily scales to handle massive datasets, something spreadsheets weren’t made for.

Machine learning plays a key role here, too. By automatically aligning and standardizing disparate integrity data, the platform removes one of the biggest roadblocks to efficient risk assessment: data silos. No more exporting and importing between teams or software. Just a unified view of your pipelines, always up to date.

Bringing Integrity and Risk Together

(Like They Should Be)

Why are risk assessments siloed from the rest of your integrity work? Why not eliminate the silo and integrate the data, making risk assessment results readily available to your integrity engineers? 

• A centralized data platform that ingests and aligns the various integrity data sets and then calculates a score for each major pipeline threat, as defined by ASME B31.8S. (Models were developed by C-FER Technologies, which is considered an expert in pipeline risk assessments.
  
• Automated dashboards that give both execs and engineers the insight they need, without hours of manual number crunching.
• On-demand risk updates that adjust dynamically as new data comes in, instead of waiting for the next manual risk review.
  

Quantitative Over Qualitative

“[T]he continuing occurrence of significant pipeline incidents points to a continuing need for operators to upgrade their tools for risk assessment and risk management.” – Overview of Methods and Tools for Improved Implementation, PHMSA Risk Modeling work group, February 01, 2020

Qualitative risk models don’t meet the needs of most pipeline operators. They’re typically dependent on opinions from Subject Matter Experts and easily become outdated.

Per the PHSMA Risk Modeling Working Group:

“In practice, continued use of qualitative and relative assessment/index models is best suited for small, less complex pipeline systems, where the effects of preventive and mitigative measures risk can be reasonably understood via changes to the model inputs. These systems can be characterized by limited geographic extent and lower mileage; simple system configuration; uniform risk factors throughout the system; affected HCAs limited in extent and similar in nature; and a single, small operating organization.”

In contrast, the Risk Management Module is built on:

• Validated models trained on 3.5 million mile-years of pipeline data.
• Monte Carlo simulations generate probabilistic outcomes to account for uncertainty and inform the operator of the true possible spectrum of PoF values.
• Failure rate calibration based on actual PHMSA incident statistics, taking the guesswork out of the unknown and aligning results with reality.

Whether it’s corrosion, ground movement, or operational error, the module quantifies the threat, not just estimates it, so you can act decisively and allocate resources where they’ll have the most impact.

Probability of Failure - Methodology

Structural Reliability-Based Models

Structural reliability theory requires loads and resistances to be modeled as probabilistic variables, which are then used to calculate the probability of failure (PoF). 

Resistance is calculated using pipe geometry and material properties such as yield strength or fracture toughness. The load is determined by the forces applied to the pipe. This includes the internal force induced by the pressure of the product and external forces/impacts. Reliability-based models are used for the following threats:

• External Corrosion, Internal Corrosion - This model simulates individual defects or a distribution of defects. The characterization of these defects is obtained from ILI measurements of both internal and external corrosion features.  
• Stress Corrosion Cracking - Two distinct forms of SCC are addressed by this model: non-classical SCC, which is characterized by trans-granular cracking in association with a near-neutral pH, and classic SCC, which is characterized by intergranular cracking in association with an alkaline (high pH) electrolyte.
• Third Party/Mechanical Damage - Mechanical damage incidents are typically caused by construction or excavation equipment working in the area of the pipeline.  The failure rate is calculated by multiplying the number of hits per unit length of pipeline by the probability of failure given a hit.  The number of hits is calculated using a fault tree analysis approach. In contrast, the probability of failure per hit is calculated using structural reliability models, which are based on the recognition that the load applied to the pipe and the capacity of the pipe to withstand the applied load are uncertain quantities.   

Historical Based Models

Historical-based models rely on data from the PHMSA incident database to build statistical predictive models.

• Equipment Failure - Pipeline failure associated with mechanical equipment is typically the result of the failure of gaskets, O-rings, seals, or packing, and the malfunction of pressure relief or regulator valves.   
• Incorrect Operational Procedures - Pipeline failures that are the direct result of incorrect operation or incorrect maintenance actions are addressed by this model.  Failures due to incorrect operation are primarily attributed to incorrect actions by control room personnel, while failures due to incorrect maintenance are primarily attributed to incorrect actions by maintenance personnel.   
• Manufacturing Defects - Pipeline failure associated with manufacturing defects is primarily attributable to fatigue growth of seam weld or pipe body defects.  Seam weld failures tend to occur in susceptible seam welds, i.e., seams with significant starter defects that undergo significant stress fluctuations due to pressure variations and/or external loads. Pipe body defects (e.g., hard spots) can also act as starter locations for planar defects that can subsequently grow to failure in fatigue.  
• Weather-Related and Outside Forces – This threat addresses geotechnical hazards involving progressive or sudden ground movement, exposure, and loss of support (e.g., river scour), as well as quantifying threat due to earthquakes. 
• Welding / Fabrication-Related - Pipeline failure associated with welding/fabrication-related defects is typically the result of failure of pipe body fittings and attachments, girth welds and couplings, or wrinkle bends. A time-independent historical-based model can be used to characterize this threat. For these defects, a common driving mechanism is secondary stresses induced by differential settlement. The geotechnical and seismic hazard models address failure due to significant progressive or sudden ground movement events, and failure due to cyclic pressure loading is addressed in the manufacturing defect fatigue model.

Who Benefits?

If you’re in pipeline integrity, risk management, or operations, the Risk Management module enables:

• Faster and more frequent threat assessments
• Better collaboration across integrity and risk teams
• High-resolution views of pipeline threats down to specific segments
• Actionable insights for maintenance planning and mitigation

In short, it enables you to move from reactive to predictive pipeline safety, while reducing the time, effort, and uncertainty that come with legacy methods.

Ushering in a new era of pipeline safety

Risk is inevitable. But how we manage it doesn’t have to be.

With the Risk Management Module, pipeline operators can break down data silos, ditch outdated spreadsheets, and finally bring integrity and risk together under one powerful platform. The result is smarter decisions, safer infrastructure, and a stronger bottom line.

Whether you're just starting your digital transformation or looking to enhance an existing integrity program, now’s the time to make the shift from static risk assessment to dynamic, data-driven risk intelligence.

Because in the pipeline world, risk never sleeps—and neither should your insight into it.

Contact us for a demo of the Risk Management module today!