A Barrier-Based Quantitative Risk Management Approach for Underground Storage of Natural Gas

Demonstrate an advanced risk assessment methodology that can help identify, prioritize, and manage underground gas storage threats in a comprehensive manner



Rochester, NY

Recipient Location



Amount Spent



Project Status

Project Result

This project was completed in 2020. The final report has been received and is under review. The project team performed a gap analysis and identified the current industry approaches to risk assessment and areas for improvement. A Hazard Identification workshop identified the threats along with relevant preventive barriers, and the results were incorporated into a bowtie model to model the likelihood of failures of the well head and down hole components. A Bayesian network framework was developed to incorporate failure modes arising from corrosion and damage, and presented to stakeholders for feedback. The integrated bowtie and Bayesian network model was completed, and successfully underwent soft validation. A webinar was held in June of 2020 to demonstrate the tool to utilities and stakeholders.

The Issue

Underground gas storage is a critical element of the natural gas infrastructure, since it helps balance the supply and demand for end users. Underground gas storage assets present unique challenges to risk assessment compared to other systems such as pipelines and power plants as the surface components and sub-surface components are closely interconnected and the failure modes of one could affect the other. Utilities are already using risk management approaches. However, factors, such as the aging of the storage system, the range of gas qualities that are coming into the pipeline, and the greater demand on gas have necessitated the need for a renewed look at the risk management methods.

Project Innovation

The project advances risk assessment by combining two complementary modeling methodologies, the bowtie and the Bayesian network methods, into an effective tool for holistic risk management of the natural gas storage systems. The bowtie approach systematically identifies all the hazards and the nature of the various safety barriers that mitigate these hazards. The Bayesian network model helps quantify the likelihood of degradation of the safety barriers that prevent hazardous events from occurring. If an operator already has these safety barriers defined, they can still utilize the Bayesian model separately to calculate the probability of failure for identified threats. Safety barriers can range from passive hardware such as well tubing or well casing to hardware that requires human intervention such as a safety valve. The Bayesian network method accounts for the interactions between various factors leading to the degradation of safety barriers and identifies leading indicators for the performance of these safety barriers so that timely mitigative actions can be taken. The project validated the proposed methods using case studies and will make a best practices document.

Project Benefits

This project advances risk assessment approaches by identifying all the hazards and the various safety barriers that mitigate these hazards by combining two models, the bowtie and Bayesian network models, that can be used separately or in combination. This is a first of its kind risk assessment approach in which the bowtie network model will be utilized as an operational risk management tool and the Bayesian network model will quantify the likelihood of degradation of the safety barriers to prevent hazardous events from occurring. The project also developed a guidance document for use as a best practice document for the risk assessment methods.

Environmental & Public Health

Environmental Sustainability

Reduced release of natural gas to the atmosphere and aquifer is an intrinsic value for California's ecosystems, water supply, aesthetics, and other resources.

Greater Reliability


California users will benefit through avoidance of gas delivery disruptions. Improved risk management practices applied systematically may be able to reduce the implementation of extremely conservative measures that may reduce or disrupt energy delivery during high demand periods.

Increase Safety


Improved understanding of threat mechanisms will help decision makers reduce the probability of failure by providing the operators with a risk-informed decision support tool.

Key Project Members

Project Member

Narasi Sridhar



Silicus Technologies, LLC.


Match Partners




Contact the Team