Gain Additional Value from Inspections

Gain Additional Value from Inspections
Plant Engineering Magazine September 2021
By Ranald Cartwright

A whole market exists to serve the inspection needs of the oil and gas industry, ready to catch defects and failures before they impede safety and operations. Yet inspec­tion can be costly, time consuming and in many cases, inefficient.

What do we mean by inefficient? Let’s take pitting as an example. When inspecting a pipe for pitting, based on material science, fluid service and a host of other factors, pitting is more likely to occur in certain areas. Target inspections proceed accordingly. However, if the inspection returns a clean bill of health, can someone really be confident that this result is reflective of the pipe’s overall condition and that risk has been man­aged effectively?

Without 100% coverage, a lack of defects and failures in inspection regimes success could come down to luck. However, while complete 100% inspection will likely deliver ultimate confidence, it’s a time consuming and expensive approach that few operators are able to undertake for every single threat. But, it is no longer the only option.

While risk-based inspection (RBI) methods go some way to focus inspection efforts, as digitalization takes hold, more intelligent and efficient approaches are becoming available, which is changing the way value is derived from inspection.

With traditional methods, asset and integrity man­agers only gain value from inspections when they identify a defect or failure; therefore, many inspec­tions return no value. With a data-driven approach to asset integrity management, all inspection outcomes provide a return.

The Value of Data

Most operators are familiar with the benefit of running statistical analysis on traditional inspection data to inform their asset integrity plan. The latest iteration of this approach takes it a step further; collating leading real-time (or near real time) data from existing opera­tional sources such as process conditions (temperature, pressure and other parameters) to create a degradation model of an operator’s assets.

The predictive integrity management plan produced is based on event-driven inspection whereby inspec­tions are primarily motivated by changing process or environmental conditions, for example, scenarios which may indicate accelerated corrosion, wall thickness loss, degradation or fatigue issues.

Every inspection carried out helps to validate the model’s assumptions about the degradation of each asset and fills in data gaps, thus building a more robust model and methodology by which inspection and integrity activities are enacted.

Operators can confidently focus their resources on areas where changing conditions require real-life valida­tion to ensure risks remain understood, and that every inspection effort made provides value to the asset team. While using a data model will see most operators expend a similar amount of inspection effort, the model con­tinuously validates the need for those inspections while significantly improving their return on investment too. Even with assets nearing end-of-life there are benefits. Anecdotally, operators using IMRANDD’s data model AIDA see a typical return on investment in less than a year and cost savings in a matter of weeks or months.

The are many immediate benefits of this new style of integrity management. Leveraging real-time data means that engineers no longer need to wait until an asset is beginning to degrade to assess condition. This change in strategy can have a significant impact on profitability as well as lengthening an asset’s lifespan. And, it can also help operators better achieve overarching business goals such as their commitment to operating sustainably and responsibly; for example, by reducing flights, optimizing resources and/or reducing fugitive emissions.

What’s more, most operators will have data readily available, which simply needs to be extracted and ana­lyzed in a different way to enable the integrity team to be more planned and effective in managing plant maintenance.

The Spirit of Regulation

Despite the obvious benefits, it can be quite difficult for asset and integrity managers to move away from the traditional mindset – not least because of the way industry sees safety and standards as one and the same. This gives little room or incentive for teams to innovate more efficient ways of managing integrity. The point of these standards is to reduce the risk of operation to as low as is reasonably practical (ALARP). However, if operators aren’t using readily available digital technologies that can further reduce risk – is ALARP being achieved? It seems that a shift in perception is still required for industry to recognize the inefficiency of relying on only historic data and lagging indicators.

Inspection results are only truly valid on the day they are taken. Yet some reports are not processed for weeks or even months – putting the operator at risk of making decisions based on outdated information. On the other hand, using real-time data enables asset and integrity managers to react quickly to potential issues before they occur, in much the same way that rotating equipment is treated. An operator would never wait to see if a turbine has developed a crack because by then it would be too late. Instead, the model is constantly assessing the data to generate early warning signs so that timely interventions can ensure safety.

In the spirit of regulation, to be responsible as an industry, people should constantly strive to go above and beyond the existing standards. With all the data that is available, that spirit is now digital. IMRANDD’s predictive modelling and machine learning solutions are a prime example of data analytics tools that push the industry to achieve more.

With the ability to gain insights that enable opera­tors to rapidly react to changing operating conditions, asset and integrity managers can at the same time have the opportunity to manage static assets more proactively, leading to reduced failure and reduced impact. It is not an incremental improvement. It’s an order of magnitude better than the traditional approach that’s been employed for the past 20 to 30 years. It’s redefining what it means to be reasonably practical.