Important implications and benefits of predictive maintenance in the oil and gas industry

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With oil and gas being asset and capital-intensive, the industry relies heavily on the performance of complex process equipment for production efficiency; as competitive pressures mount, companies are seeking help from technology. With regulatory, environmental, and competitive pressures set only to grow, increased industry investment in predictive maintenance will be inevitable.

Implications of that investment and some benefits to be achieved by early adopters.

Implications
Increased investment in PdM technology will create a virtuous circle of technology development. Here are two areas where growth will occur:

Increased virtual monitoring
Artificial intelligence and machine learning have accelerated in maturity with increased focus and investment, allowing companies to automate field operations and processes. This automation has several drivers, not least the labour shortages we’re experiencing now, which will only worsen over time.

Another catalyst to automate is the cost and difficulty of using operators to collect data and adjust operating criteria. Transferring data to a centralized database is cumbersome, does not occur in real-time, and there is potential for human error. While PdM will still operate on the data, such manual systems do not realize the full potential of predictive maintenance.

When Petroleum Development Oman introduced asset management software to automate its operations, it saw benefits beyond maintenance. Yes, processes were faster with automated workflow triggers, and the resolution of issues and routine tasks was more rapid. However, the added benefits were seen in ordering spares, where the previous 46 steps were reduced to 14, saving one full-time equivalent. Also, visibility into the facility’s management increased with integrated performance dashboards.

Increased use of PdM and Physics-based modeling
Companies have been reticent to automate because of the cost, complexity, and time to generate a meaningful return on investment (ROI). Technology adoption leads to a greater number of products being introduced to the market, and their prices continue to drop, accelerating growth further. With the cost issue receding, companies will now point to complexity and ROI as the next barriers to adoption.

Typical PdM solutions require extensive time from data scientists to gather and clean maintenance data before training the machine learning models. However, an emerging solution is using physics-based modeling to speed up PdM implementation. Physics-based, digital simulations help predict how the equipment will fail, providing greater certainty, speed of implementation, and advance notice of failure when compared to purely data-driven PdM.

Benefits
There are two main tactical and operational benefits for oil and gas companies using PdM:

Improved equipment availability
Kimberlite, a market research firm that specializes in collecting market data for the oil and gas industry, claim that less than four days of unplanned downtime costs an oil and gas company over $5 million, with most companies averaging 27 days of annual unscheduled downtime.

Meanwhile, McKinsey has carried out numerous studies into oil and gas operations. Their findings suggest that operators using PdM achieve a five to 10 per cent improvement in production efficiency, and a 20 to 30 per cent decrease in maintenance costs compared to contemporaries using time-driven maintenance philosophies. Additionally, they claim a 30 to 50 per cent reduction in downtime on critical machines.

Findings from the US Department of Energy support McKinsey’s figures, with PdM implementation in oil and gas providing a 25 to 30 per cent reduction in maintenance costs, a 70 to 75 per cent reduction in breakdowns, and a 35 to 45 per cent reduction in downtime. They also show production increases between 20 to 25 per cent, and a 10 times ROI.

Enhanced environmental and safety performance
As both the investors and the public demand increased performance from oil and gas companies in matters of the environment and sustainability, predictive maintenance offers an opportunity for serious improvements. Unplanned shutdowns can cause product release through leaks or purging, poorly functioning equipment can consume greater energy, and equipment failure can lead to safety issues and environmental contamination.

PdM gives advance notice of operating parameters trending away from in-service norms, despite these characteristics being unnoticeable by human senses. Alerts allow the planning and procurement for a planned maintenance intervention, and — where possible — changes to the operating process to militate failure until repairs take place.

One oil and gas operator discovered almost three-quarters of all flaring emissions occurred due to poor equipment reliability. By implementing PdM, the company reduced its emissions significantly and increased production. Another operator created algorithms to predict gas compressor train failures, achieving 70 per cent accuracy and increasing production by almost 0.5 per cent.

The take-up of PdM in the oil and gas industry has been surprisingly slow, with predominantly larger companies investing. However, this reluctance will change as technology costs continue to reduce, and the numbers of available PdM solutions increases. Oil and gas companies face strong headwinds with increased competition from renewables, regulatory oversight, and societal demands for greater accountability, environmental performance, and sustainability.

Advanced analytics is something the oil and gas industry can use to cope with the varied demands, investing in machine learning, Ai, and predictive algorithms, to improve operating asset monitoring, management, and control. The return on investment experienced by those who have taken the plunge, has been good due to improved production, reduced maintenance costs, and fewer breakdowns.
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Bryan Christiansen is the Founder and CEO at Limble CMMS (a mobile CMMS software company). He can be reached at bryan@limblecmms.com.