An Introduction to Risk-Based Inspection

Risk-Based Inspection (RBI) is a proven asset integrity management method for prioritizing inspection activities, identifying and understanding risk drivers, and creating effective risk mitigation strategies. For complex processing industries, knowing where to focus inspection spend, when to perform inspections, and which methods will provide the most confidence in understanding an asset’s current state is crucial, as this information helps facility leaders decide how to prioritize inspection budgets and reduce loss of containment events.

Conditions that drive risk are not always intuitive. A structured, consistent, systematic process like RBI helps uncover issues that are not always evident and those that are. Sometimes the answer is inspection, other times the correct action might be installation of a lining or cladding, and RBI may provide justification for changing materials of construction to achieve the desired level of risk or reliability. RBI, done right, is a holistic process and requires looking at the equipment as part of a dynamic system.

Quality data is at the core of any RBI program, but how can those responsible for asset integrity easily and effectively manage this information? With thousands of assets to monitor, it can be challenging to get the data right and keep it evergreen. More often than not, knowing where to prioritize data clean-up and data collection efforts can make the difference between a sustainable or unsustainable RBI program.

Let’s take a look at what Risk-Based Inspection is, how it’s helped the industry, what its limitations are, and what the future of reliability looks like.

What Is Risk-Based Inspection?

Risk-Based Inspection (RBI) is an asset integrity management methodology that uses probability of failure (PoF) and consequence of failure (CoF) to calculate risk for individual assets. The risk and risk drivers identified for each asset are then used to prioritize and drive inspection strategies that define where to focus inspection efforts, when to perform inspections, and which methods to use for greatest effectiveness. RBI will also define which action(s) is (are) most appropriate to mitigate risk (e.g., inspection to increase confidence in the true damage state of the equipment, repair, replacement, lining installation, introducing a corrosion inhibitor, etc.).

By targeting areas of high-risk and performing prescribed inspections, complex process facilities can reduce unplanned failures, decrease overall loss of containment risk, and increase the effectiveness of their inspection programs. RBI is most prominently used in the downstream refining and chemical industries due to compliance requirements and the chemical processes these facilities manage. With that said, many industries—such as mining and wastewater—have adapted RBI to evolve their existing asset management programs. RBI, in many jurisdictions, will permit, when appropriate, on-stream inspections, which reduce risks and costs with entering equipment.

For most processing facilities, Risk-Based Inspection is guided by the American Petroleum Institute (API)’s recommended practices, API RP 580, Risk-Based Inspection and API RP 581, Risk-Based Inspection Technology. API RP 580 details the minimum requirements and guidelines for implementing an effective Risk-based Inspection program and API RP 581 provides recommended procedures and methodologies to be used in a RBI program.

Having an effective, compliant RBI program represents a key advancement toward compliance with the U.S. Occupational Safety and Health Administration (OSHA) Process Safety Management (PSM) regulation. This regulation was created to ensure safety for facilities that contain hazardous chemicals. The rule requires process facilities with hazardous fluids to have documented plans addressing the assets containing these highly hazardous chemicals. Specifically, the PSM plans must help facilities understand, identify, manage, and reduce the risks associated with operating.

Risk-Based Inspection: A Revolution in Inspection Planning

Before Risk-Based Inspection, oil and gas companies achieved asset integrity through a Time-Based Approach (TBA). A time-based approach is highly inefficient because it often results in a large amount of resources being spent disproportionally to the risk of each asset. This dated practice also does little to encourage the operator to understand units, systems and loops, or the interdependent effects of operating practices on the equipment. A TBA is thought of as looking at equipment from an “after the fact” perspective and is often referred to as driving a car forward by looking in the rearview mirror. Whereas, RBI considers past, current, and future operating practices and their effects on the integrity of the equipment.

In the early 2000s, RBI was created to focus this resource spend. RBI was a game-changer because it drove mechanical integrity programs to be managed by risk, not time. RBI prioritizes inspections by risk and ensures resources are spent on the most troublesome assets, in the right manner, at the right times. By using risk as a guide, facilities operate more proactively, efficiently, and effectively.

Risk-Based Inspection Limitations

When it comes to managing inspections, an RBI program is a great first step after a time-based program in the evolution of inspection program maturity. However, when it comes to further optimizing and improving system reliability performance, we need to look at the next evolution after RBI. Specific limitations currently include the following:

  • RBI does not calculate absolute risk; rather, it calculates relative risk, which is used to drive inspection priorities rather than provide an objective cost/benefit analysis. In addition, knock-on effects typically are not considered.
  • RBI models are typically very conservative due to core assumptions around generic failure frequencies, and debits are applied for lack of data or information, the calculation of probability for failure, and consequence of failure—particularly around health, safety, and environmental (HSE) risks. Additionally, over-conservatism often leads to dubious financial risk numbers where financial output is used. Once financial risk is used, most managers forget the results are based on relative instead of absolute risk (i.e., the dollar projections are relative).
  • RBI only calculates loss of containment risk (not functional failure) and helps drive inspection priorities accordingly.
  • RBI calculations occur on an asset-by-asset bases and do not relate to the overall performance of the system, unit, or facility.
  • RBI cannot be used to optimize an entire system, unit, or facility’s reliability strategy based on availability, cost, and resource constraints.
  • RBI does not help quantify the value of data collection or help with sensitivity analysis of required data for calculations beyond manual Iteration of values from the user.

What Does the Future of Reliability Data Look Like?

RBI, among other reliability technologies, has helped the industry become not only more reliable and safer, but more cost effective in deploying resources. However, the industry is continuing to see major advancements in data acquisition, processing, warehousing, modeling, curation, and analytics. With these capabilities in hand, we now have the opportunity to take the next leap in reliability analysis, allowing us to further improve reliability while decreasing total maintenance and inspection spend.

This leap is made possible through Quantitative Reliability Optimization (QRO). QRO is an approach to reliability modeling which connects every relevant reliability data point at a complex facility to one integrated model, enabling near real-time effective decision making for complex systems. QRO allows users to do things such as:

  • Optimize, in near real-time, all maintenance spend based on short/mid/long-term reliability targets.
  • Understand the economic value of every inspection or maintenance activity performed.
  • Understand the economic value of every piece of data that is currently being gathered or could possibly be gathered in the future.
  • Near real-time scenario modeling, including the implications of moving a turnaround, changing feedstock pricing, or introducing various capital projects.
  • Drive effective economic decisions in the event of reliability based operating excursions.

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