Why drones are revolutionizing asset inspection in oil and gas

In my previous blog, I looked at operational excellence in oil and gas. In its Oil and Gas Trends 2018-19, PwC suggests that companies should ‘double down’ on digitization to drive operational excellence, citing the use of drones to inspect offshore platforms as a key example. So, how can you make the most of drones within your asset inspection processes?

There’s always a slight question mark surrounding ‘disruptive’ technologies. We have a lot of hype but little in the way of actual application on the ground. No doubt technologies like augmented reality will have their day, but it’s not here yet.

Things are different when it comes to drones. Today, the markets is being driven by commercial adoption, with “drone pilot” now one of the top 20 job searches in the US. Training schools are estimated to be creating a new drone pilot workforce of around 400,000 – to give you a comparison that’s more than all the private school teachers in the US.

The major beneficiary of this new workforce is oil and gas. Given the nature of its infrastructure – and the incredibly hazardous nature of monitoring and maintaining it – drones offer a solution to asset inspection that is both far more effective and far more cost-effective than anything that was previously possible. The costs and dangers of inspecting a flare stack, say, are very large indeed.

A radically better approach to asset inspection

Drones can effortlessly inspect the miles of pipeline, oil rigs, refineries and plant, flare stacks and underwater equipment. Armed with video and sensors, drones can monitor for leaks, detect oil spills, ensure pipeline and rigs are safe and compliant. In fact, drones are able to detect and monitor elements such as gas emissions that can be invisible to the human eye and extremely difficult to spot in a manual inspection process.

For companies where margins are tight, drones deliver a way to regularly inspect assets without have to suspend production. They also don’t have to put staff or contractors in harm’s way in order to complete the task. Information from the drone is fed back to maintenance technicians who can analyze the data and take the correct action.

As the inspection now only amounts to the cost of the drone flight and the pilot’s and technician’s time, then inspections can take place on an almost on-going basis. The cost savings are very impressive. Some industry estimates suggest that using drones for asset inspection will save as much as $1.1 billion a year for oil rigs and refineries.

Drones are only half the answer

Drones provide more in-depth and complete information on an asset than has been possible through manual inspection. However, this only leads to benefits if that information is properly utilized. To truly maximize the value of this new source of information, it must be properly captured, enriched and managed in a frictionless manner. Historically, this is something where industry has performed poorly. Forbes notes that 60% of operators admit that delivering outcomes from data is a major problem for them.

Companies must clearly understand what they are going to do with the drone data once it has been captured and how users are going to access it. Without this context, inspection data runs the risk of being relegated to sit in a file system or worse on someone’s PC. This makes it difficult for inspection teams to know what data exists for a particular asset or find related information for comparisons to detect any changes over time.

Mismanagement of the drone generated data has the potential to simply confuse the inspection engineers, who now have too much data to process. Ultimately this will negate any or all of the benefits that are trying to be realized from the improved data capture capability.

The alternative is to use the drones, and the drone inspection data, to move from a manual asset inspection process to end-to-end digital asset inspection one, where the information is inserted into the correct stages of the process to automatically trigger the next stage.

For example, a drone captures images of corrosion in a section of pipeline. The video file is returned to the maintenance technician who performs an initial review and then adds the video file to the relevant asset documentation repository (automatically adding relevant metadata, GPS, date and time data) and informs the quality engineer that they should take a look. The quality engineer assesses the footage and decides a repair needs to be made. They create a work order that is automatically populated with all the relevant information and dispatched to the field engineer to schedule the work. This is all conducted within the same enterprise-wide content management system so that you are assured that the information is fully up-to-date at every stage of the process.

From proactive to predictive maintenance

By creating a central information platform for content management and analysis, you can automate many steps within your asset inspection process, enabling a far more proactive approach to maintenance that is both safe and cost-effective. It also provides a historical store of data that allows you to apply AI-powered analytics in order to spot patterns and trends, identify anomalies and drive better decision-making around asset utilization and maintenance. It enables you to take more effective steps towards Predictive Maintenance to keep assets performing for longer and significantly reducing downtime.

Martin Richards

Martin Richards is a Senior Director for Energy Industry Solutions at OpenText. For over twenty years, he has worked with ECM technology, delivering professional services and solutions for the energy and engineering industry.

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One Comment

  1. Nice one Martin, I saw similar things from the innovation department of Shell at an IoT conference in 2016. This was transformational for them in the inspection of flair stacks. They didn’t have to shut the stacks down to inspect them and were receiving new insights from the drone work inspecting them running hot. I guess there is a crossover point as adoption grows where disruptive tech becomes transforming tech and that’s where the pay off are.

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