Powering oil and gas operations with AI

Oil and gas production has never been more challenging. Expensive ageing infrastructure scattered all over the globe, coupled with an increased demand for oil and mounting regulatory pressure for cleaner, greener operations has made energy production harder than ever. The current approach to inspections doesn’t make sense from a staffing, efficiency, and profitability standpoint.

Traditionally, manual inspections and maintenance methods – reactive or preventive – are still prevalent, but these approaches are increasingly proving inadequate. They are costly, time-consuming, and leave room for failures that disrupt production.

This is where AI-driven maintenance has arrived, transforming how energy plants are monitored and maintained. This ensures uninterrupted production while creating efficiencies and reducing operating costs. An investment in these maintenance approaches can deliver a 5x ROI or more, by extending the life of equipment, protecting production, improving operator safety, and reducing excess energy consumption.

Breaking down maintenance complexities in oil and gas

The oil and gas sector has had many challenges in the last few years. While oil prices have always been somewhat volatile, the subsequent supply change issues have made matters even worse. This, coupled with mounting pressure to meet stringent emissions targets with a dwindling talent pool of experienced operators, quickly adds up to be a formidable undertaking.

While the sector has always been good at ensuring production by building in redundancy where possible, downtime is still a very real concern. Plus, with increased pressures on the industry, the challenge to keep things flowing faster, cheaper, and cleaner is greater than ever before.

Lost production time and product is a concern in any manufacturing industry. Unfortunately, the oil and gas sector has several other obstacles to overcome as well.

• Scale: the machines themselves are massive and the plants incredibly complex. Keeping tabs on all aspects is challenging enough without having to deal with expensive, ageing equipment and infrastructure.
• Volume: the sheer volume of product moving through a refinery at any given time means that downtime is not only costly but has lasting effects downstream.
• Location: plants are built where the oil is, ensuring that infrastructure can be found in some of the remotest parts of the world. This can make regular onsite testing and maintenance, not to mention replacement parts and repairs, challenging and expensive.

How traditional maintenance approaches fall short

Critical systems in refineries are often designed and built with a redundant backup system in place. Unfortunately, this isn’t the most efficient way of operating and a run- to-failure maintenance strategy isn’t recommended, as it can result in collateral damage to the plant, personnel, and environment.

Another common approach is route-based, manual testing of the equipment’s vital signs. This expensive, time-consuming approach to monitoring is intermittent and merely provides a snapshot of where the machine is at, with nothing known between measurements, and given the often-remote conditions, there can be sometime between measurements.

The more advanced plants may have moved to a vibrational or multi-modal sensor to detect issues. While this does give a better idea of the equipment’s current state, it doesn’t give real insight into its remaining useful life or diagnosing issues or energy savings!

The evolution of maintenance strategies 

The digital transformation of Industry 4.0 brings a paradigm shift in maintenance strategies: 

• Reactive maintenance: repairing equipment after a failure occurs. 
• Preventive maintenance: scheduling maintenance at set intervals, regardless of equipment condition.
• Predictive and prescriptive maintenance: going beyond just predictions, it diagnoses issues, estimates useful life, and recommends specific corrective actions.
• Energy centred maintenance: this approach allows machines to reduce operating costs by lowering that energy consumption, ensuring uptime and ultimately reduces greenhouse gas emissions.

The power of AI-driven maintenance

Production quality and quantity are dependent on the efficiency and efficacy of the machinery being used. Leveraging prescriptive maintenance, maintenance teams gain unprecedented views into machine’s health. Knowing which machine needs attention and how best to keep them up and running, ensures meeting the production targets.

What else is possible?

• Sustainable operations: faulty equipment is shown to consume more electricity. This increased demand needlessly increases operating expenses, while expanding your organisation’s carbon footprint.
• Energy centred maintenance reveals what equipment needs maintenance and consumes additional energy. Thus, not only helping to protect your batch but tangibly reducing your operating expenses and environmental impact.
• Efficient staffing: staffing is becoming a growing concern, as finding skilled talent and keeping them has become a global challenge. Thankfully shifting to a proactive, prescriptive maintenance model empowers less skilled labour to identify and correct equipment issues. Limiting the need for a robust team of highly skilled labourers and ensuring you’re still running an accurate, efficient maintenance operation.
• Operator safety: insights into machine health empower operators to schedule maintenance before faults become failures threatening operator safety.

Not to mention, it’s been shown that half of plant injuries happen during unplanned downtime, while everyone is frantically trying to make urgent repairs to get back up and running.

Critical assets across the energy value chain

Ongoing monitoring is essential for the performance and longevity of critical oil and gas assets. Key components requiring constant monitoring, pumps, fracking, mud, and reciprocating, are central to upstream and midstream operations. Compressors, turbines, and fans are downstream. Additionally, gearboxes and artificial lifts need to be closely observed to prevent breakdowns. The same is also true for sucker rod pumps and centrifugal pumps.

Stringent cybersecurity measures are required to safeguard the vast amounts of data collected from these assets, especially given the limited bandwidth for communication with cloud servers in remote locations. Furthermore, continuously variable speed equipment, which is often found in pumps and compressors, demands specialised monitoring.

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  Predictive and prescriptive maintenance: going beyond just predictions, it diagnoses issues, estimates useful life, and recommends specific corrective actions. 

Read the article online at: https://www.oilfieldtechnology.com/special-reports/31032025/powering-oil-and-gas-operations-with-ai/