The Industry Challenge:
Public perception of the risk posed by operations in the energy industry continues to increase, causing operators and regulators to make unprecedented efforts to demonstrate their ability to understand and manage these risks.
At the same time, the drive to make operations more efficient has led operators to consider risk-based models to help guide decisions.
Many risk analysis approaches use a grid-based system to rank the probability of occurrence of an event and the severity of the consequences of that event to identify events that pose the highest perceived risk.
These systems are typically qualitative, relying on input from experts to rank the probabilities and consequences. Some systems assign “order of magnitude” values to these rankings to simplify and compare risk levels; however, it is difficult to be consistent in assigning these values without strict guidelines.
It is also difficult to use these risk-values to drive inspection, maintenance and repair decisions since the scoring systems do not provide measurable values where an operator can compare the cost of these activities to how much the risk is reduced.
Regulators are requesting more quantitative risk-based models to increase traceability, objectivity and consistency of risk assessments.
This includes incorporating various engineering models that account for the uncertainties inherent in the analysis such as measurement accuracy, variability in material properties and manufacturing tolerances.
A comprehensive risk analyses must also consider a broad spectrum of threats including damage mechanisms as diverse as third‑party damage, corrosion and manufacturing defects that could lead to failures. Similarly, analysis of the consequences of events must use a consistent framework to evaluate the impact in terms of environmental, economic and life safety.
How We Help:
C-FER helps operators understand the risk posed by complex engineering systems and expresses the risk in terms that can provide direction in the decision-making process. The analysis uses engineering models to evaluate the probability of failure of the system and the associated consequences of those failures in terms of life safety, environmental and economic impacts.
The first step in the analysis is to identify the various threats and hazards that could affect the asset. This can include considering upsets in operating conditions such as pressure transients, degradation of the equipment condition due to damage such as corrosion and fatigue, and external events such as storms, ground movement and third‑party damage.
Fault trees can be used when combinations of basic events need to occur in order to cause a failure in complex, multi-component systems. These fault trees are developed with the client’s own subject matter experts to ensure that all threats and failure modes are considered. The fault tree is then populated by assigning probabilities of occurrence to each event in the fault tree using historical data and engineering models.
Uncertainties in the inputs to the probability models are accommodated by assigning a distribution that describes the range of possible values. The input distributions are sampled randomly to solve an engineering model using the Monte Carlo method. Combining the outcomes of each random scenario results in a distribution of outcomes that is used to estimate the probability of failure of the system.
Specialized analysis tools are used to identify the factors that have the greatest influence on the overall risk so that inspection, maintenance and repair activities focus on the critical issues.
This includes activities to reduce the probability of failure such as repairing damaged equipment or to minimize the consequences of an event such as installing monitoring systems to reduce the response time to an incident.
The cost to implement these options can be compared to the amount the risk reduction to identify the activities that will provide the greatest risk reduction for the least cost. Where targets for tolerable risk levels are mandated, a similar approach can be used to evaluate which activities need to be implemented to achieve the risk target.
Examples where a risk-based or reliability-based approach have been applied include:
- Pipeline integrity management;
- Gas storage in salt caverns and depleted reservoirs;
- Critical sour drilling operations; and
- Thermal well integrity.