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Efficiency drive

Published:  08 December, 2015

Automated condition monitoring could help the UK oil and gas industry to regain higher levels of production efficiency – and help to banish unplanned shutdowns. ODEE reports.

Production in the UK oil and gas industry has been steadily declining in recent years. In 2005, annual production of crude oil was almost 80 million tonnes, but this had halved to 40 million tonnes by 2013.

The dip has gone hand in hand with a steady fall in production efficiency – the ratio between actual production and the maximum production potential. Over the same period, it fell from just over 80% to a low of 60% in 2012, recovering marginally in 2013. Unplanned plant shutdowns accounted for nearly half of all losses, while annual shutdowns accounted for one-fifth – meaning that any measures to address these could have a healthy effect on the bottom line.

There are many consequences of low production efficiency: reduced taxable profits; early closure of a particular oilfield or gas field; and a loss of jobs. It’s for these and many other reasons why it is in the best interests of the oil and gas industry to improve efficiency.

At the same time, North Sea costs have continued to rise – far faster than UK inflation, or in other oil-producing regions, according to management consultant McKinsey.

“The North Sea has historically been one of the most cost-efficient areas in global oil and gas,” said a McKinsey report. “Less than 15 years ago, it had average unit lifting costs of £3-5/BOE (Barrel of Oil Equivalent) and average development costs of £4-5.”

Since then, lifting costs in the region has skyrocketed to £17/BOE – twice what it was just five years ago, says the report. In some fields, that cost is £30/BOE. If lifting costs continue to rise at 10% per year – while production is declining at 7% – this cost will rise to £100/BOE within seven years, making extraction from many fields uneconomic.

Saving costs

It all seems inexorable, but McKinsey says that cost savings – and efficiency gains – can be made by ‘digitising’ oil and gas production. By this, it means that automation technology, such as sensors and control systems, can help to reduce unplanned downtime.

“Automation creates several opportunities in terms of optimising production efficiency: maximising asset and well integrity, increasing field recovery and improving oil throughput,” says the company.

Even small improvements in efficiency can have a substantial effect on profitability, because such huge production volumes are involved.

“Even in the low-volume regimes of current unconventional mature assets – oil sands, for example – carefully targeted automation can cut costs and improve the reliability of production equipment, leading to higher revenues,” says McKinsey.

Many critical factors, such as supply chain inefficiencies or the need to regenerate infrastructure, are outside the scope of automation and unlikely to benefit from it directly. However, improving automation, in all its forms, will help to tackle some of the industry’s most pressing problems – including some that would appear surprising.

For example, the industry is gearing up for a ‘big crew change’ – in that many experienced professionals are close to retirement. This is likely to cause a huge gap in knowledge and expertise, which retention and recruitment are unlikely to fill completely. While automation may seem an unlikely saviour here, it could play a vital role – by capturing the details of many routine analyses and decision-support processes, and automating them. This kind of ‘knowledge capture’ is becoming more common across industry.

Maintaining production

One of the critical areas where automation is already proven is maintenance. Preventative maintenance, which uses information from sensors to predict when failure is likely to occur – and take action before it happens – is far more efficient than the traditional reactive maintenance. It is already well-proven within the general manufacturing industry, and continues to grow in popularity.

The technique lends itself perfectly to oil production. Just as a manufacturing plant requires ongoing maintenance to maximise productivity, the same is true of oil platforms. But while a modern oilfield may be replete with any number of sensors and other digitally enabled systems, the challenge is to harness all the data that it can provide – and make sense of it.

“Converting this complex flood of data into better business and operating decisions requires new, carefully designed capabilities for data manipulation, analysis and presentation – as well as tools to support decision making,” says McKinsey.

Many of these condition-based monitoring tools already exist, and are used widely across the manufacturing sector. A common example is vibration analysis, in which vibration sensors monitor the performance of bearings in assets such as motors or fans. It can detect when a bearing is beginning to fail – and alert maintenance operators to replace it.

For example, Phil Burge, country communication manager at SKF, explained to PWE, that the SKF Wireless Machine Condition Sensor combines a sensor, data collector and radio into a compact, battery operated device that measures both vibration and temperature data. The system, he highlights, is approved for use in hazardous areas, transmits static and dynamic data to SKF @ptitude Monitoring Suite software, and offers a simple, reliable and secure means of expanding condition-based maintenance into areas where the cost to install wired systems in prohibitive – making data available to existing process control and information systems.

Burge added that process data from components such as motors, fans and pumps, can be collected using handheld devices – such as the SKF Microlog Analyzer CMXA 51-IS – but automatic data collection is becoming far more prevalent. In this case, he explains, fixed sensors monitor assets constantly and transmit the data automatically to a central point, where it can be analysed. The SKF Multilog On-line System IMx-M is a good example of this: in conjunction with the SKF @ptitude Monitoring Suite software, it can flag up early fault detection and carry out fault diagnosis.

Pros and Cons

The advantages of adopting this kind of approach are many and varied. The most telling is that the maintenance team is never taken by surprise; by keeping a constant watch on all critical components, technicians – with assistance from remote experts, who help to interpret the mass of data – are on hand to repair or replace parts before they fail.

This means that their time is used in the most efficient way, with none wasted on ‘scheduled’ maintenance that ends up being unnecessary. Also, most critically, it ensures that the catastrophic failures that lead to expensive shut-downs are minimised – or even abolished completely.

Stepping up to condition-based monitoring is a real leap forward, and both a financial and strategic commitment. There are many examples in industry – in the marine sector, for example – where ingrained conservatism or strict legislation has prevented its wider adoption. In addition, there is certainly the challenge of getting to grips with this extra complexity, but this is not really an issue for on-site technicians – because the data can be fed back to a central point for expert interpretation.

The advantages are clear. If there are any downsides, the main one would be the cost of investment: imbuing the whole of a plant or oil rig with sensor capability – and the necessary support services to make full use of this new data – is not cheap. However, if used wisely, the added benefits from this information will far outweigh the initial investment costs.

“There is clear competitive imperative for increasing automation in oil and gas production,” says McKinsey. “Companies that successfully implement big data and analytics, sensors and other new technologies will be well positioned to meet their industry’s challenges.”

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