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November 2017 Special Report: Coping with dynamic needs of the industry

by Indrajit Sen on Nov 12, 2017


Automation service providers have to keep their offering fluid enough to effectively cater to the oil and gas industry.
Automation service providers have to keep their offering fluid enough to effectively cater to the oil and gas industry.

In this rapidly changing energy landscape, the nature of and demand for digitalisation and automation technology in the oil and gas industry is also undergoing transformation.

This implies that although providers of automation services can be content with the way products from their portfolio are being deployed by their customers today, the increasing role of automation in upstream operations will lead to new specific demands from those same customers.

Suppliers also have to brace for the fact that a compact portfolio of automation products for the oil and gas clientele may not yield much returns in the days to come, as requirements become niche and operational issues become complex. Therefore, service providers have to keep their offering fluid enough to effectively cater to the oil and gas industry. 

According to the American industrial software services provider AspenTech, which has been doing business with key oil and gas operators for about 35 years now, despite the company’s established expertise in software, technology and industry expertise, it is having to combine its latest machine learning and analytics capabilities to create an automation portfolio for the future. 

Luc Chantepy, regional sales vice president for the Middle East and North Africa region at AspenTech, explains: “Today, customers have three needs: to monitor performance, because what you can measure, you can improve. Second, they need to implement those improvements. The competitive environment that has emerged in just a few short years requires a more holistic approach, which is where asset optimisation comes in. For example, with advanced machine learning software, companies have already demonstrated incredible successes in the early identification of equipment failure. Such software is near-autonomous and learns behavioural patterns from the streams of digital data that are produced by sensors on and around machines and processes. Automatically, and requiring minimal resources, this advanced technology constantly learns and adapts to new signal patterns when operating conditions change.”

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